firenet150x50_len22_5_trainval

I0407 12:20:09.825912 32304 solver.cpp:280] Solving 
I0407 12:20:09.825924 32304 solver.cpp:281] Learning Rate Policy: poly
I0407 12:20:10.069551 32304 solver.cpp:229] Iteration 0, loss = 4.30406
I0407 12:20:10.069617 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0
I0407 12:20:10.069638 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:20:10.069651 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:20:10.069664 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0
I0407 12:20:10.069674 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0
I0407 12:20:10.069711 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0
I0407 12:20:10.069725 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0
I0407 12:20:10.069736 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0
I0407 12:20:10.069747 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0
I0407 12:20:10.069759 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0
I0407 12:20:10.069771 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 0
I0407 12:20:10.069782 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 0
I0407 12:20:10.069793 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 0
I0407 12:20:10.069805 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 0
I0407 12:20:10.069816 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 0
I0407 12:20:10.069828 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 0
I0407 12:20:10.069839 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 0
I0407 12:20:10.069850 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 0
I0407 12:20:10.069861 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 0
I0407 12:20:10.069872 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 0
I0407 12:20:10.069883 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 0
I0407 12:20:10.069895 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 0
I0407 12:20:10.069914 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 4.30407 (* 0.0454545 = 0.19564 loss)
I0407 12:20:10.069929 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.30394 (* 0.0454545 = 0.195634 loss)
I0407 12:20:10.069943 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.3041 (* 0.0454545 = 0.195641 loss)
I0407 12:20:10.069957 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 4.30411 (* 0.0454545 = 0.195641 loss)
I0407 12:20:10.069970 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 4.30407 (* 0.0454545 = 0.19564 loss)
I0407 12:20:10.069984 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 4.30415 (* 0.0454545 = 0.195643 loss)
I0407 12:20:10.069998 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 4.30406 (* 0.0454545 = 0.195639 loss)
I0407 12:20:10.070011 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 4.30406 (* 0.0454545 = 0.195639 loss)
I0407 12:20:10.070025 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 4.30429 (* 0.0454545 = 0.19565 loss)
I0407 12:20:10.070039 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 4.30432 (* 0.0454545 = 0.195651 loss)
I0407 12:20:10.070052 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.30414 (* 0.0454545 = 0.195643 loss)
I0407 12:20:10.070071 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.30418 (* 0.0454545 = 0.195644 loss)
I0407 12:20:10.070086 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.30363 (* 0.0454545 = 0.19562 loss)
I0407 12:20:10.070101 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.30404 (* 0.0454545 = 0.195638 loss)
I0407 12:20:10.070114 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.30432 (* 0.0454545 = 0.195651 loss)
I0407 12:20:10.070127 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.30364 (* 0.0454545 = 0.19562 loss)
I0407 12:20:10.070142 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.30361 (* 0.0454545 = 0.195619 loss)
I0407 12:20:10.070155 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.30437 (* 0.0454545 = 0.195653 loss)
I0407 12:20:10.070168 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.30412 (* 0.0454545 = 0.195642 loss)
I0407 12:20:10.070183 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.30433 (* 0.0454545 = 0.195651 loss)
I0407 12:20:10.070195 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.30374 (* 0.0454545 = 0.195625 loss)
I0407 12:20:10.070220 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.30399 (* 0.0454545 = 0.195636 loss)
I0407 12:20:10.070232 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:20:10.070243 32304 solver.cpp:245]     Train net output #45: total_confidence = 7.67211e-42
I0407 12:20:10.070268 32304 sgd_solver.cpp:106] Iteration 0, lr = 0.01
I0407 12:22:23.474257 32304 solver.cpp:229] Iteration 500, loss = 2.0143
I0407 12:22:23.474453 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:22:23.474473 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 12:22:23.474488 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:22:23.474498 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0
I0407 12:22:23.474510 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0
I0407 12:22:23.474521 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 12:22:23.474534 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 12:22:23.474545 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 12:22:23.474557 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:22:23.474570 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 12:22:23.474581 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:22:23.474592 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:22:23.474604 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:22:23.474617 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:22:23.474628 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:22:23.474640 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:22:23.474652 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:22:23.474663 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:22:23.474674 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:22:23.474685 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:22:23.474697 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:22:23.474709 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:22:23.474725 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 4.18064 (* 0.0454545 = 0.190029 loss)
I0407 12:22:23.474738 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.94447 (* 0.0454545 = 0.179294 loss)
I0407 12:22:23.474752 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.20165 (* 0.0454545 = 0.190984 loss)
I0407 12:22:23.474766 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 4 (* 0.0454545 = 0.181818 loss)
I0407 12:22:23.474781 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 4.13148 (* 0.0454545 = 0.187795 loss)
I0407 12:22:23.474793 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.18788 (* 0.0454545 = 0.144904 loss)
I0407 12:22:23.474807 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 3.46623 (* 0.0454545 = 0.157556 loss)
I0407 12:22:23.474822 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.04117 (* 0.0454545 = 0.0473259 loss)
I0407 12:22:23.474835 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.223171 (* 0.0454545 = 0.0101441 loss)
I0407 12:22:23.474848 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.227835 (* 0.0454545 = 0.0103561 loss)
I0407 12:22:23.474863 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000821093 (* 0.0454545 = 3.73224e-05 loss)
I0407 12:22:23.474876 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000812151 (* 0.0454545 = 3.69159e-05 loss)
I0407 12:22:23.474890 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000825878 (* 0.0454545 = 3.75399e-05 loss)
I0407 12:22:23.474905 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000834139 (* 0.0454545 = 3.79154e-05 loss)
I0407 12:22:23.474920 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.00085137 (* 0.0454545 = 3.86986e-05 loss)
I0407 12:22:23.474934 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000823922 (* 0.0454545 = 3.7451e-05 loss)
I0407 12:22:23.474949 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000829594 (* 0.0454545 = 3.77088e-05 loss)
I0407 12:22:23.474982 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000800383 (* 0.0454545 = 3.6381e-05 loss)
I0407 12:22:23.474997 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000825226 (* 0.0454545 = 3.75103e-05 loss)
I0407 12:22:23.475010 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000804606 (* 0.0454545 = 3.6573e-05 loss)
I0407 12:22:23.475024 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000831463 (* 0.0454545 = 3.77938e-05 loss)
I0407 12:22:23.475039 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000808299 (* 0.0454545 = 3.67409e-05 loss)
I0407 12:22:23.475050 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:22:23.475064 32304 solver.cpp:245]     Train net output #45: total_confidence = 4.66638e-09
I0407 12:22:23.475078 32304 sgd_solver.cpp:106] Iteration 500, lr = 0.00999
I0407 12:23:49.824404 32304 solver.cpp:229] Iteration 1000, loss = 1.26772
I0407 12:23:49.824553 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 12:23:49.824573 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:23:49.824585 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:23:49.824599 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:23:49.824610 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.0625
I0407 12:23:49.824623 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 12:23:49.824635 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.59375
I0407 12:23:49.824646 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 12:23:49.824658 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 12:23:49.824671 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 12:23:49.824681 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:23:49.824693 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:23:49.824705 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:23:49.824717 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:23:49.824728 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:23:49.824740 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:23:49.824751 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:23:49.824762 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:23:49.824774 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:23:49.824785 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:23:49.824797 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:23:49.824808 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:23:49.824823 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.69766 (* 0.0454545 = 0.168076 loss)
I0407 12:23:49.824838 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.26349 (* 0.0454545 = 0.193795 loss)
I0407 12:23:49.824851 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.09787 (* 0.0454545 = 0.186267 loss)
I0407 12:23:49.824865 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.97539 (* 0.0454545 = 0.1807 loss)
I0407 12:23:49.824879 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 4.00708 (* 0.0454545 = 0.18214 loss)
I0407 12:23:49.824892 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.27293 (* 0.0454545 = 0.148769 loss)
I0407 12:23:49.824906 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 3.0471 (* 0.0454545 = 0.138505 loss)
I0407 12:23:49.824923 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.948792 (* 0.0454545 = 0.0431269 loss)
I0407 12:23:49.824937 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.658232 (* 0.0454545 = 0.0299196 loss)
I0407 12:23:49.824951 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.522186 (* 0.0454545 = 0.0237357 loss)
I0407 12:23:49.824965 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000490595 (* 0.0454545 = 2.22998e-05 loss)
I0407 12:23:49.824980 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000473515 (* 0.0454545 = 2.15234e-05 loss)
I0407 12:23:49.824993 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000469622 (* 0.0454545 = 2.13464e-05 loss)
I0407 12:23:49.825007 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000465248 (* 0.0454545 = 2.11476e-05 loss)
I0407 12:23:49.825021 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000478604 (* 0.0454545 = 2.17547e-05 loss)
I0407 12:23:49.825036 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000488342 (* 0.0454545 = 2.21974e-05 loss)
I0407 12:23:49.825049 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.00048748 (* 0.0454545 = 2.21582e-05 loss)
I0407 12:23:49.825076 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000472075 (* 0.0454545 = 2.1458e-05 loss)
I0407 12:23:49.825091 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000473624 (* 0.0454545 = 2.15284e-05 loss)
I0407 12:23:49.825105 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000476492 (* 0.0454545 = 2.16587e-05 loss)
I0407 12:23:49.825120 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000479562 (* 0.0454545 = 2.17983e-05 loss)
I0407 12:23:49.825134 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000468949 (* 0.0454545 = 2.13159e-05 loss)
I0407 12:23:49.825146 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:23:49.825158 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.68859e-08
I0407 12:23:49.825172 32304 sgd_solver.cpp:106] Iteration 1000, lr = 0.00998
I0407 12:25:11.535065 32304 solver.cpp:229] Iteration 1500, loss = 1.23664
I0407 12:25:11.535243 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 12:25:11.535279 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 12:25:11.535305 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 12:25:11.535327 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 12:25:11.535367 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.03125
I0407 12:25:11.535395 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 12:25:11.535419 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 12:25:11.535441 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 12:25:11.535464 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:25:11.535486 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:25:11.535507 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:25:11.535528 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:25:11.535550 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:25:11.535574 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:25:11.535596 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:25:11.535619 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:25:11.535640 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:25:11.535662 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:25:11.535684 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:25:11.535707 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:25:11.535728 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:25:11.535753 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:25:11.535781 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 4.11474 (* 0.0454545 = 0.187033 loss)
I0407 12:25:11.535809 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.00535 (* 0.0454545 = 0.182061 loss)
I0407 12:25:11.535836 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.19491 (* 0.0454545 = 0.190678 loss)
I0407 12:25:11.535863 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 4.09272 (* 0.0454545 = 0.186033 loss)
I0407 12:25:11.535897 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 4.02348 (* 0.0454545 = 0.182885 loss)
I0407 12:25:11.535930 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.93325 (* 0.0454545 = 0.133329 loss)
I0407 12:25:11.535950 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.00432 (* 0.0454545 = 0.0911054 loss)
I0407 12:25:11.535965 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.679386 (* 0.0454545 = 0.0308812 loss)
I0407 12:25:11.535979 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.263788 (* 0.0454545 = 0.0119904 loss)
I0407 12:25:11.535994 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0199751 (* 0.0454545 = 0.000907959 loss)
I0407 12:25:11.536008 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000505204 (* 0.0454545 = 2.29638e-05 loss)
I0407 12:25:11.536022 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000504179 (* 0.0454545 = 2.29172e-05 loss)
I0407 12:25:11.536036 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000497386 (* 0.0454545 = 2.26085e-05 loss)
I0407 12:25:11.536051 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000491688 (* 0.0454545 = 2.23494e-05 loss)
I0407 12:25:11.536064 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000498138 (* 0.0454545 = 2.26426e-05 loss)
I0407 12:25:11.536078 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000499396 (* 0.0454545 = 2.26998e-05 loss)
I0407 12:25:11.536092 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000493848 (* 0.0454545 = 2.24476e-05 loss)
I0407 12:25:11.536125 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000504339 (* 0.0454545 = 2.29245e-05 loss)
I0407 12:25:11.536141 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000499358 (* 0.0454545 = 2.26981e-05 loss)
I0407 12:25:11.536154 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000495203 (* 0.0454545 = 2.25092e-05 loss)
I0407 12:25:11.536170 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000500192 (* 0.0454545 = 2.2736e-05 loss)
I0407 12:25:11.536182 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000488204 (* 0.0454545 = 2.21911e-05 loss)
I0407 12:25:11.536195 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:25:11.536206 32304 solver.cpp:245]     Train net output #45: total_confidence = 3.98502e-08
I0407 12:25:11.536221 32304 sgd_solver.cpp:106] Iteration 1500, lr = 0.00997
I0407 12:26:29.127930 32304 solver.cpp:229] Iteration 2000, loss = 1.20731
I0407 12:26:29.128075 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:26:29.128096 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:26:29.128109 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:26:29.128121 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:26:29.128134 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.0625
I0407 12:26:29.128144 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 12:26:29.128156 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 12:26:29.128168 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 12:26:29.128180 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 12:26:29.128191 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:26:29.128202 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:26:29.128214 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:26:29.128226 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:26:29.128238 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:26:29.128249 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:26:29.128260 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:26:29.128273 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:26:29.128283 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:26:29.128294 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:26:29.128305 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:26:29.128317 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:26:29.128329 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:26:29.128343 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 4.05749 (* 0.0454545 = 0.184431 loss)
I0407 12:26:29.128358 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.07136 (* 0.0454545 = 0.185062 loss)
I0407 12:26:29.128372 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.92102 (* 0.0454545 = 0.178228 loss)
I0407 12:26:29.128386 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 4.01917 (* 0.0454545 = 0.182689 loss)
I0407 12:26:29.128398 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 4.00457 (* 0.0454545 = 0.182026 loss)
I0407 12:26:29.128412 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.01885 (* 0.0454545 = 0.13722 loss)
I0407 12:26:29.128427 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.39069 (* 0.0454545 = 0.108668 loss)
I0407 12:26:29.128440 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.00523 (* 0.0454545 = 0.0456922 loss)
I0407 12:26:29.128453 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.532893 (* 0.0454545 = 0.0242224 loss)
I0407 12:26:29.128468 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0174102 (* 0.0454545 = 0.000791371 loss)
I0407 12:26:29.128481 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000708007 (* 0.0454545 = 3.21821e-05 loss)
I0407 12:26:29.128495 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000706536 (* 0.0454545 = 3.21153e-05 loss)
I0407 12:26:29.128510 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000709158 (* 0.0454545 = 3.22345e-05 loss)
I0407 12:26:29.128523 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000701596 (* 0.0454545 = 3.18907e-05 loss)
I0407 12:26:29.128537 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000723607 (* 0.0454545 = 3.28912e-05 loss)
I0407 12:26:29.128551 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000720892 (* 0.0454545 = 3.27678e-05 loss)
I0407 12:26:29.128566 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000727574 (* 0.0454545 = 3.30716e-05 loss)
I0407 12:26:29.128597 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.00070631 (* 0.0454545 = 3.2105e-05 loss)
I0407 12:26:29.128612 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.00068794 (* 0.0454545 = 3.127e-05 loss)
I0407 12:26:29.128626 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.00070613 (* 0.0454545 = 3.20968e-05 loss)
I0407 12:26:29.128641 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.0007029 (* 0.0454545 = 3.195e-05 loss)
I0407 12:26:29.128655 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000720051 (* 0.0454545 = 3.27296e-05 loss)
I0407 12:26:29.128667 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:26:29.128679 32304 solver.cpp:245]     Train net output #45: total_confidence = 4.71931e-08
I0407 12:26:29.128692 32304 sgd_solver.cpp:106] Iteration 2000, lr = 0.00996
I0407 12:27:45.564164 32304 solver.cpp:229] Iteration 2500, loss = 1.19892
I0407 12:27:45.564308 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 12:27:45.564329 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 12:27:45.564342 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:27:45.564362 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:27:45.564373 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.03125
I0407 12:27:45.564384 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 12:27:45.564396 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 12:27:45.564409 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 12:27:45.564420 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:27:45.564432 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 12:27:45.564443 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:27:45.564455 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:27:45.564466 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:27:45.564478 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:27:45.564497 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:27:45.564508 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:27:45.564520 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:27:45.564532 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:27:45.564543 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:27:45.564559 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:27:45.564570 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:27:45.564581 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:27:45.564597 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.81302 (* 0.0454545 = 0.173319 loss)
I0407 12:27:45.564612 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.7516 (* 0.0454545 = 0.170527 loss)
I0407 12:27:45.564625 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.2194 (* 0.0454545 = 0.191791 loss)
I0407 12:27:45.564638 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.93634 (* 0.0454545 = 0.178925 loss)
I0407 12:27:45.564652 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 4.09091 (* 0.0454545 = 0.185951 loss)
I0407 12:27:45.564666 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.82298 (* 0.0454545 = 0.128318 loss)
I0407 12:27:45.564679 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.0546 (* 0.0454545 = 0.0933911 loss)
I0407 12:27:45.564697 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.602191 (* 0.0454545 = 0.0273723 loss)
I0407 12:27:45.564710 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.218529 (* 0.0454545 = 0.00993312 loss)
I0407 12:27:45.564724 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.22974 (* 0.0454545 = 0.0104427 loss)
I0407 12:27:45.564738 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.00121821 (* 0.0454545 = 5.53734e-05 loss)
I0407 12:27:45.564759 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00120117 (* 0.0454545 = 5.45986e-05 loss)
I0407 12:27:45.564772 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.00122883 (* 0.0454545 = 5.58558e-05 loss)
I0407 12:27:45.564786 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00122205 (* 0.0454545 = 5.55478e-05 loss)
I0407 12:27:45.564800 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.00121609 (* 0.0454545 = 5.5277e-05 loss)
I0407 12:27:45.564815 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.00124584 (* 0.0454545 = 5.66292e-05 loss)
I0407 12:27:45.564828 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.00124706 (* 0.0454545 = 5.66844e-05 loss)
I0407 12:27:45.564859 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.00121587 (* 0.0454545 = 5.5267e-05 loss)
I0407 12:27:45.564874 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.00122863 (* 0.0454545 = 5.5847e-05 loss)
I0407 12:27:45.564888 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.00121812 (* 0.0454545 = 5.5369e-05 loss)
I0407 12:27:45.564903 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.00122209 (* 0.0454545 = 5.55497e-05 loss)
I0407 12:27:45.564915 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.00121066 (* 0.0454545 = 5.50301e-05 loss)
I0407 12:27:45.564931 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:27:45.564942 32304 solver.cpp:245]     Train net output #45: total_confidence = 7.27128e-08
I0407 12:27:45.564962 32304 sgd_solver.cpp:106] Iteration 2500, lr = 0.00995
I0407 12:29:01.267943 32304 solver.cpp:229] Iteration 3000, loss = 1.19266
I0407 12:29:01.268126 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:29:01.268156 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:29:01.268168 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:29:01.268180 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 12:29:01.268193 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.0625
I0407 12:29:01.268204 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.21875
I0407 12:29:01.268216 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.5
I0407 12:29:01.268229 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.71875
I0407 12:29:01.268240 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.875
I0407 12:29:01.268252 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 12:29:01.268265 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:29:01.268276 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:29:01.268288 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:29:01.268299 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:29:01.268311 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:29:01.268322 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:29:01.268333 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:29:01.268344 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:29:01.268355 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:29:01.268368 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:29:01.268379 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:29:01.268390 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:29:01.268406 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 4.14847 (* 0.0454545 = 0.188567 loss)
I0407 12:29:01.268421 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.09765 (* 0.0454545 = 0.186257 loss)
I0407 12:29:01.268435 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.00209 (* 0.0454545 = 0.181913 loss)
I0407 12:29:01.268448 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.98447 (* 0.0454545 = 0.181112 loss)
I0407 12:29:01.268461 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 4.06968 (* 0.0454545 = 0.184985 loss)
I0407 12:29:01.268476 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.51329 (* 0.0454545 = 0.159695 loss)
I0407 12:29:01.268489 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.8334 (* 0.0454545 = 0.128791 loss)
I0407 12:29:01.268503 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.62427 (* 0.0454545 = 0.0738304 loss)
I0407 12:29:01.268517 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.80327 (* 0.0454545 = 0.0365123 loss)
I0407 12:29:01.268530 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.415718 (* 0.0454545 = 0.0188963 loss)
I0407 12:29:01.268544 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000738587 (* 0.0454545 = 3.35722e-05 loss)
I0407 12:29:01.268558 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00072699 (* 0.0454545 = 3.3045e-05 loss)
I0407 12:29:01.268573 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000725413 (* 0.0454545 = 3.29733e-05 loss)
I0407 12:29:01.268586 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000713091 (* 0.0454545 = 3.24132e-05 loss)
I0407 12:29:01.268601 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.00074435 (* 0.0454545 = 3.38341e-05 loss)
I0407 12:29:01.268622 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000743226 (* 0.0454545 = 3.3783e-05 loss)
I0407 12:29:01.268636 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000743904 (* 0.0454545 = 3.38138e-05 loss)
I0407 12:29:01.268663 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000727551 (* 0.0454545 = 3.30705e-05 loss)
I0407 12:29:01.268678 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000740102 (* 0.0454545 = 3.3641e-05 loss)
I0407 12:29:01.268699 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000721387 (* 0.0454545 = 3.27903e-05 loss)
I0407 12:29:01.268713 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000741564 (* 0.0454545 = 3.37074e-05 loss)
I0407 12:29:01.268728 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000722842 (* 0.0454545 = 3.28565e-05 loss)
I0407 12:29:01.268739 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:29:01.268751 32304 solver.cpp:245]     Train net output #45: total_confidence = 7.20107e-08
I0407 12:29:01.268769 32304 sgd_solver.cpp:106] Iteration 3000, lr = 0.00994
I0407 12:30:16.183094 32304 solver.cpp:229] Iteration 3500, loss = 1.18104
I0407 12:30:16.183244 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0
I0407 12:30:16.183265 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:30:16.183277 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.15625
I0407 12:30:16.183291 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:30:16.183303 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 12:30:16.183315 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.28125
I0407 12:30:16.183327 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 12:30:16.183339 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 12:30:16.183351 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 12:30:16.183379 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:30:16.183393 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:30:16.183404 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:30:16.183418 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:30:16.183429 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:30:16.183439 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:30:16.183451 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:30:16.183462 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:30:16.183475 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:30:16.183486 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:30:16.183497 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:30:16.183508 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:30:16.183521 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:30:16.183537 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.99525 (* 0.0454545 = 0.181602 loss)
I0407 12:30:16.183550 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.15301 (* 0.0454545 = 0.188773 loss)
I0407 12:30:16.183564 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.92377 (* 0.0454545 = 0.178353 loss)
I0407 12:30:16.183578 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 4.05085 (* 0.0454545 = 0.18413 loss)
I0407 12:30:16.183593 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.86581 (* 0.0454545 = 0.175719 loss)
I0407 12:30:16.183606 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.50374 (* 0.0454545 = 0.159261 loss)
I0407 12:30:16.183620 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.09121 (* 0.0454545 = 0.0950551 loss)
I0407 12:30:16.183634 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.82091 (* 0.0454545 = 0.0373141 loss)
I0407 12:30:16.183647 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0477853 (* 0.0454545 = 0.00217206 loss)
I0407 12:30:16.183661 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.016161 (* 0.0454545 = 0.000734592 loss)
I0407 12:30:16.183676 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000422762 (* 0.0454545 = 1.92165e-05 loss)
I0407 12:30:16.183689 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000408989 (* 0.0454545 = 1.85904e-05 loss)
I0407 12:30:16.183703 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000416637 (* 0.0454545 = 1.8938e-05 loss)
I0407 12:30:16.183717 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000416785 (* 0.0454545 = 1.89448e-05 loss)
I0407 12:30:16.183732 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000419601 (* 0.0454545 = 1.90728e-05 loss)
I0407 12:30:16.183744 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.00042529 (* 0.0454545 = 1.93314e-05 loss)
I0407 12:30:16.183758 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000419825 (* 0.0454545 = 1.90829e-05 loss)
I0407 12:30:16.183791 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000422241 (* 0.0454545 = 1.91928e-05 loss)
I0407 12:30:16.183805 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000417211 (* 0.0454545 = 1.89641e-05 loss)
I0407 12:30:16.183820 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000421187 (* 0.0454545 = 1.91448e-05 loss)
I0407 12:30:16.183833 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000415347 (* 0.0454545 = 1.88794e-05 loss)
I0407 12:30:16.183847 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000410226 (* 0.0454545 = 1.86466e-05 loss)
I0407 12:30:16.183859 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:30:16.183871 32304 solver.cpp:245]     Train net output #45: total_confidence = 5.36788e-08
I0407 12:30:16.183886 32304 sgd_solver.cpp:106] Iteration 3500, lr = 0.00993
I0407 12:31:30.619607 32304 solver.cpp:229] Iteration 4000, loss = 1.17466
I0407 12:31:30.619747 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 12:31:30.619767 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 12:31:30.619781 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:31:30.619792 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 12:31:30.619806 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 12:31:30.619817 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 12:31:30.619829 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 12:31:30.619840 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 12:31:30.619853 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:31:30.619863 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:31:30.619875 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:31:30.619886 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:31:30.619899 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:31:30.619910 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:31:30.619921 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:31:30.619932 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:31:30.619943 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:31:30.619956 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:31:30.619966 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:31:30.619978 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:31:30.619989 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:31:30.620000 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:31:30.620017 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.77438 (* 0.0454545 = 0.171563 loss)
I0407 12:31:30.620030 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.90407 (* 0.0454545 = 0.177458 loss)
I0407 12:31:30.620044 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.04361 (* 0.0454545 = 0.1838 loss)
I0407 12:31:30.620059 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.91976 (* 0.0454545 = 0.178171 loss)
I0407 12:31:30.620085 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.97482 (* 0.0454545 = 0.180674 loss)
I0407 12:31:30.620110 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.25777 (* 0.0454545 = 0.14808 loss)
I0407 12:31:30.620126 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.91153 (* 0.0454545 = 0.0868878 loss)
I0407 12:31:30.620138 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.424502 (* 0.0454545 = 0.0192956 loss)
I0407 12:31:30.620152 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.229537 (* 0.0454545 = 0.0104335 loss)
I0407 12:31:30.620167 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0134987 (* 0.0454545 = 0.000613578 loss)
I0407 12:31:30.620182 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000429615 (* 0.0454545 = 1.9528e-05 loss)
I0407 12:31:30.620198 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00043214 (* 0.0454545 = 1.96427e-05 loss)
I0407 12:31:30.620213 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000430642 (* 0.0454545 = 1.95746e-05 loss)
I0407 12:31:30.620228 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000431972 (* 0.0454545 = 1.96351e-05 loss)
I0407 12:31:30.620241 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000424682 (* 0.0454545 = 1.93037e-05 loss)
I0407 12:31:30.620254 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000428989 (* 0.0454545 = 1.94995e-05 loss)
I0407 12:31:30.620268 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000424718 (* 0.0454545 = 1.93054e-05 loss)
I0407 12:31:30.620301 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000425363 (* 0.0454545 = 1.93347e-05 loss)
I0407 12:31:30.620316 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000426001 (* 0.0454545 = 1.93637e-05 loss)
I0407 12:31:30.620329 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000422961 (* 0.0454545 = 1.92255e-05 loss)
I0407 12:31:30.620342 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000431076 (* 0.0454545 = 1.95944e-05 loss)
I0407 12:31:30.620357 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.0004344 (* 0.0454545 = 1.97455e-05 loss)
I0407 12:31:30.620368 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:31:30.620380 32304 solver.cpp:245]     Train net output #45: total_confidence = 1.16314e-07
I0407 12:31:30.620395 32304 sgd_solver.cpp:106] Iteration 4000, lr = 0.00992
I0407 12:32:46.624099 32304 solver.cpp:229] Iteration 4500, loss = 1.17085
I0407 12:32:46.624194 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:32:46.624214 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 12:32:46.624228 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:32:46.624239 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:32:46.624251 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.40625
I0407 12:32:46.624264 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.59375
I0407 12:32:46.624275 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 12:32:46.624287 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 12:32:46.624299 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 12:32:46.624310 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.90625
I0407 12:32:46.624322 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:32:46.624333 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:32:46.624346 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:32:46.624357 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:32:46.624369 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:32:46.624380 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:32:46.624392 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:32:46.624403 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:32:46.624414 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:32:46.624426 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:32:46.624438 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:32:46.624449 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:32:46.624465 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.90805 (* 0.0454545 = 0.177639 loss)
I0407 12:32:46.624480 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.02798 (* 0.0454545 = 0.18309 loss)
I0407 12:32:46.624493 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.21417 (* 0.0454545 = 0.191553 loss)
I0407 12:32:46.624506 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.98033 (* 0.0454545 = 0.180924 loss)
I0407 12:32:46.624519 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.50098 (* 0.0454545 = 0.159135 loss)
I0407 12:32:46.624533 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.24061 (* 0.0454545 = 0.101846 loss)
I0407 12:32:46.624547 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.01879 (* 0.0454545 = 0.0917632 loss)
I0407 12:32:46.624562 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.967614 (* 0.0454545 = 0.0439824 loss)
I0407 12:32:46.624575 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.557235 (* 0.0454545 = 0.0253289 loss)
I0407 12:32:46.624588 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.601343 (* 0.0454545 = 0.0273338 loss)
I0407 12:32:46.624603 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000947406 (* 0.0454545 = 4.30639e-05 loss)
I0407 12:32:46.624617 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000928938 (* 0.0454545 = 4.22244e-05 loss)
I0407 12:32:46.624631 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000931879 (* 0.0454545 = 4.23581e-05 loss)
I0407 12:32:46.624644 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000959022 (* 0.0454545 = 4.35919e-05 loss)
I0407 12:32:46.624658 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000921981 (* 0.0454545 = 4.19082e-05 loss)
I0407 12:32:46.624672 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000944025 (* 0.0454545 = 4.29102e-05 loss)
I0407 12:32:46.624686 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.00095566 (* 0.0454545 = 4.34391e-05 loss)
I0407 12:32:46.624716 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000919401 (* 0.0454545 = 4.1791e-05 loss)
I0407 12:32:46.624732 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000928263 (* 0.0454545 = 4.21938e-05 loss)
I0407 12:32:46.624745 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000947601 (* 0.0454545 = 4.30728e-05 loss)
I0407 12:32:46.624759 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000927157 (* 0.0454545 = 4.21435e-05 loss)
I0407 12:32:46.624774 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000946088 (* 0.0454545 = 4.3004e-05 loss)
I0407 12:32:46.624786 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:32:46.624797 32304 solver.cpp:245]     Train net output #45: total_confidence = 1.43383e-07
I0407 12:32:46.624811 32304 sgd_solver.cpp:106] Iteration 4500, lr = 0.00991
I0407 12:34:00.316678 32304 solver.cpp:338] Iteration 5000, Testing net (#0)
I0407 12:34:12.183802 32304 solver.cpp:393] Test loss: 1.02799
I0407 12:34:12.183862 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.511
I0407 12:34:12.183879 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.062
I0407 12:34:12.183893 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.068
I0407 12:34:12.183905 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.012
I0407 12:34:12.183919 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.207
I0407 12:34:12.183933 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.501
I0407 12:34:12.183943 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.894
I0407 12:34:12.183955 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 12:34:12.183966 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 12:34:12.183977 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 12:34:12.183990 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 12:34:12.184000 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 12:34:12.184012 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 12:34:12.184023 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 12:34:12.184034 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 12:34:12.184046 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 12:34:12.184056 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 12:34:12.184067 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 12:34:12.184078 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 12:34:12.184090 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 12:34:12.184101 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 12:34:12.184113 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 12:34:12.184128 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 3.18957 (* 0.0454545 = 0.14498 loss)
I0407 12:34:12.184144 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.68944 (* 0.0454545 = 0.167702 loss)
I0407 12:34:12.184156 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 4.01099 (* 0.0454545 = 0.182318 loss)
I0407 12:34:12.184170 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 3.90182 (* 0.0454545 = 0.177356 loss)
I0407 12:34:12.184183 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 3.92507 (* 0.0454545 = 0.178412 loss)
I0407 12:34:12.184196 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 2.54965 (* 0.0454545 = 0.115893 loss)
I0407 12:34:12.184209 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.941849 (* 0.0454545 = 0.0428113 loss)
I0407 12:34:12.184222 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.298501 (* 0.0454545 = 0.0135682 loss)
I0407 12:34:12.184237 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0730203 (* 0.0454545 = 0.0033191 loss)
I0407 12:34:12.184250 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0311105 (* 0.0454545 = 0.00141411 loss)
I0407 12:34:12.184264 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 0.000406098 (* 0.0454545 = 1.8459e-05 loss)
I0407 12:34:12.184278 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 0.000400885 (* 0.0454545 = 1.8222e-05 loss)
I0407 12:34:12.184291 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 0.000406289 (* 0.0454545 = 1.84677e-05 loss)
I0407 12:34:12.184305 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 0.000404574 (* 0.0454545 = 1.83897e-05 loss)
I0407 12:34:12.184319 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 0.000404385 (* 0.0454545 = 1.83811e-05 loss)
I0407 12:34:12.184332 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 0.000404669 (* 0.0454545 = 1.8394e-05 loss)
I0407 12:34:12.184346 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 0.000403631 (* 0.0454545 = 1.83469e-05 loss)
I0407 12:34:12.184393 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 0.000404048 (* 0.0454545 = 1.83658e-05 loss)
I0407 12:34:12.184409 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 0.000403554 (* 0.0454545 = 1.83434e-05 loss)
I0407 12:34:12.184423 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 0.00040514 (* 0.0454545 = 1.84154e-05 loss)
I0407 12:34:12.184438 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 0.000403396 (* 0.0454545 = 1.83362e-05 loss)
I0407 12:34:12.184451 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 0.000405912 (* 0.0454545 = 1.84506e-05 loss)
I0407 12:34:12.184463 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0
I0407 12:34:12.184475 32304 solver.cpp:406]     Test net output #45: total_confidence = 3.67987e-07
I0407 12:34:12.219660 32304 solver.cpp:229] Iteration 5000, loss = 1.16957
I0407 12:34:12.219717 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 12:34:12.219735 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 12:34:12.219748 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:34:12.219760 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0
I0407 12:34:12.219772 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 12:34:12.219784 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 12:34:12.219796 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 12:34:12.219808 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 12:34:12.219820 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:34:12.219835 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 12:34:12.219858 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:34:12.219880 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:34:12.219902 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:34:12.219923 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:34:12.219944 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:34:12.219974 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:34:12.219988 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:34:12.220000 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:34:12.220011 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:34:12.220022 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:34:12.220034 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:34:12.220046 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:34:12.220062 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.88541 (* 0.0454545 = 0.17661 loss)
I0407 12:34:12.220077 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.86529 (* 0.0454545 = 0.175695 loss)
I0407 12:34:12.220091 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.97672 (* 0.0454545 = 0.18076 loss)
I0407 12:34:12.220116 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.9315 (* 0.0454545 = 0.178705 loss)
I0407 12:34:12.220147 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.77379 (* 0.0454545 = 0.171536 loss)
I0407 12:34:12.220176 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.84556 (* 0.0454545 = 0.129344 loss)
I0407 12:34:12.220194 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.73083 (* 0.0454545 = 0.0786743 loss)
I0407 12:34:12.220208 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.834465 (* 0.0454545 = 0.0379302 loss)
I0407 12:34:12.220222 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.265347 (* 0.0454545 = 0.0120612 loss)
I0407 12:34:12.220237 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.289552 (* 0.0454545 = 0.0131615 loss)
I0407 12:34:12.220273 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000411549 (* 0.0454545 = 1.87068e-05 loss)
I0407 12:34:12.220289 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000412992 (* 0.0454545 = 1.87724e-05 loss)
I0407 12:34:12.220304 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000417731 (* 0.0454545 = 1.89878e-05 loss)
I0407 12:34:12.220317 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000419737 (* 0.0454545 = 1.9079e-05 loss)
I0407 12:34:12.220331 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000421235 (* 0.0454545 = 1.91471e-05 loss)
I0407 12:34:12.220345 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.00042681 (* 0.0454545 = 1.94005e-05 loss)
I0407 12:34:12.220360 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000415686 (* 0.0454545 = 1.88948e-05 loss)
I0407 12:34:12.220372 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000398535 (* 0.0454545 = 1.81152e-05 loss)
I0407 12:34:12.220386 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000414276 (* 0.0454545 = 1.88307e-05 loss)
I0407 12:34:12.220399 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000416906 (* 0.0454545 = 1.89503e-05 loss)
I0407 12:34:12.220412 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000419003 (* 0.0454545 = 1.90456e-05 loss)
I0407 12:34:12.220427 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000401184 (* 0.0454545 = 1.82356e-05 loss)
I0407 12:34:12.220438 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:34:12.220449 32304 solver.cpp:245]     Train net output #45: total_confidence = 3.60229e-07
I0407 12:34:12.220464 32304 sgd_solver.cpp:106] Iteration 5000, lr = 0.0099
I0407 12:35:26.158509 32304 solver.cpp:229] Iteration 5500, loss = 1.1667
I0407 12:35:26.158653 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:35:26.158682 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:35:26.158696 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:35:26.158709 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 12:35:26.158720 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 12:35:26.158732 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 12:35:26.158745 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.5625
I0407 12:35:26.158764 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 12:35:26.158776 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 12:35:26.158787 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 12:35:26.158799 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:35:26.158810 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:35:26.158823 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:35:26.158834 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:35:26.158845 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:35:26.158857 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:35:26.158869 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:35:26.158880 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:35:26.158891 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:35:26.158902 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:35:26.158915 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:35:26.158929 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:35:26.158946 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.9415 (* 0.0454545 = 0.179159 loss)
I0407 12:35:26.158959 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.12022 (* 0.0454545 = 0.187283 loss)
I0407 12:35:26.158980 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.13614 (* 0.0454545 = 0.188006 loss)
I0407 12:35:26.158994 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 4.02197 (* 0.0454545 = 0.182817 loss)
I0407 12:35:26.159008 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.61876 (* 0.0454545 = 0.164489 loss)
I0407 12:35:26.159020 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.90736 (* 0.0454545 = 0.132153 loss)
I0407 12:35:26.159042 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.48835 (* 0.0454545 = 0.113107 loss)
I0407 12:35:26.159056 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.98904 (* 0.0454545 = 0.0449563 loss)
I0407 12:35:26.159070 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.648744 (* 0.0454545 = 0.0294884 loss)
I0407 12:35:26.159085 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.462304 (* 0.0454545 = 0.0210138 loss)
I0407 12:35:26.159098 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000555089 (* 0.0454545 = 2.52313e-05 loss)
I0407 12:35:26.159112 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000559372 (* 0.0454545 = 2.5426e-05 loss)
I0407 12:35:26.159132 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000567596 (* 0.0454545 = 2.57998e-05 loss)
I0407 12:35:26.159145 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000557174 (* 0.0454545 = 2.53261e-05 loss)
I0407 12:35:26.159158 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000559473 (* 0.0454545 = 2.54306e-05 loss)
I0407 12:35:26.159173 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000545992 (* 0.0454545 = 2.48178e-05 loss)
I0407 12:35:26.159193 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000557796 (* 0.0454545 = 2.53544e-05 loss)
I0407 12:35:26.159224 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.00056676 (* 0.0454545 = 2.57618e-05 loss)
I0407 12:35:26.159240 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.00055462 (* 0.0454545 = 2.521e-05 loss)
I0407 12:35:26.159255 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000566455 (* 0.0454545 = 2.57479e-05 loss)
I0407 12:35:26.159268 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000562946 (* 0.0454545 = 2.55885e-05 loss)
I0407 12:35:26.159281 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000564383 (* 0.0454545 = 2.56538e-05 loss)
I0407 12:35:26.159293 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:35:26.159306 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.72196e-07
I0407 12:35:26.159333 32304 sgd_solver.cpp:106] Iteration 5500, lr = 0.00989
I0407 12:36:38.895735 32304 solver.cpp:229] Iteration 6000, loss = 1.16152
I0407 12:36:38.895845 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:36:38.895866 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:36:38.895879 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:36:38.895890 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 12:36:38.895903 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 12:36:38.895915 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 12:36:38.895926 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 12:36:38.895939 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 12:36:38.895951 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 12:36:38.895962 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:36:38.895974 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:36:38.895985 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:36:38.895997 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:36:38.896008 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:36:38.896019 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:36:38.896030 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:36:38.896041 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:36:38.896054 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:36:38.896064 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:36:38.896075 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:36:38.896087 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:36:38.896098 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:36:38.896114 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.6616 (* 0.0454545 = 0.166436 loss)
I0407 12:36:38.896145 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.89099 (* 0.0454545 = 0.176863 loss)
I0407 12:36:38.896167 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.89617 (* 0.0454545 = 0.177098 loss)
I0407 12:36:38.896183 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.87326 (* 0.0454545 = 0.176057 loss)
I0407 12:36:38.896196 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.65102 (* 0.0454545 = 0.165955 loss)
I0407 12:36:38.896210 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.05084 (* 0.0454545 = 0.138675 loss)
I0407 12:36:38.896224 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.32771 (* 0.0454545 = 0.0603506 loss)
I0407 12:36:38.896239 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.695931 (* 0.0454545 = 0.0316332 loss)
I0407 12:36:38.896252 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0421042 (* 0.0454545 = 0.00191383 loss)
I0407 12:36:38.896267 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0155951 (* 0.0454545 = 0.000708868 loss)
I0407 12:36:38.896281 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000361046 (* 0.0454545 = 1.64112e-05 loss)
I0407 12:36:38.896296 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000343916 (* 0.0454545 = 1.56325e-05 loss)
I0407 12:36:38.896309 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000351553 (* 0.0454545 = 1.59797e-05 loss)
I0407 12:36:38.896323 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000354718 (* 0.0454545 = 1.61236e-05 loss)
I0407 12:36:38.896337 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000357903 (* 0.0454545 = 1.62683e-05 loss)
I0407 12:36:38.896352 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000349797 (* 0.0454545 = 1.58999e-05 loss)
I0407 12:36:38.896365 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000357156 (* 0.0454545 = 1.62344e-05 loss)
I0407 12:36:38.896396 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000361773 (* 0.0454545 = 1.64442e-05 loss)
I0407 12:36:38.896411 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000357877 (* 0.0454545 = 1.62671e-05 loss)
I0407 12:36:38.896425 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000359385 (* 0.0454545 = 1.63357e-05 loss)
I0407 12:36:38.896438 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000363899 (* 0.0454545 = 1.65409e-05 loss)
I0407 12:36:38.896452 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000373879 (* 0.0454545 = 1.69945e-05 loss)
I0407 12:36:38.896463 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:36:38.896476 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.50444e-07
I0407 12:36:38.896489 32304 sgd_solver.cpp:106] Iteration 6000, lr = 0.00988
I0407 12:37:51.982652 32304 solver.cpp:229] Iteration 6500, loss = 1.15957
I0407 12:37:51.982841 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 12:37:51.982862 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 12:37:51.982880 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 12:37:51.982893 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 12:37:51.982905 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 12:37:51.982919 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 12:37:51.982939 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 12:37:51.982950 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 12:37:51.982962 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:37:51.982974 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:37:51.982985 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:37:51.983005 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:37:51.983016 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:37:51.983027 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:37:51.983038 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:37:51.983049 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:37:51.983060 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:37:51.983072 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:37:51.983083 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:37:51.983094 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:37:51.983106 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:37:51.983117 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:37:51.983141 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.82889 (* 0.0454545 = 0.174041 loss)
I0407 12:37:51.983155 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.97756 (* 0.0454545 = 0.180798 loss)
I0407 12:37:51.983170 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.08898 (* 0.0454545 = 0.185863 loss)
I0407 12:37:51.983182 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.8943 (* 0.0454545 = 0.177014 loss)
I0407 12:37:51.983196 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.71896 (* 0.0454545 = 0.169044 loss)
I0407 12:37:51.983217 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.15009 (* 0.0454545 = 0.143186 loss)
I0407 12:37:51.983229 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.14272 (* 0.0454545 = 0.0973963 loss)
I0407 12:37:51.983243 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.990116 (* 0.0454545 = 0.0450053 loss)
I0407 12:37:51.983258 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.205082 (* 0.0454545 = 0.00932189 loss)
I0407 12:37:51.983279 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0199406 (* 0.0454545 = 0.000906391 loss)
I0407 12:37:51.983294 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000517626 (* 0.0454545 = 2.35285e-05 loss)
I0407 12:37:51.983307 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000511826 (* 0.0454545 = 2.32648e-05 loss)
I0407 12:37:51.983335 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000509906 (* 0.0454545 = 2.31775e-05 loss)
I0407 12:37:51.983360 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000510497 (* 0.0454545 = 2.32044e-05 loss)
I0407 12:37:51.983373 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000508185 (* 0.0454545 = 2.30993e-05 loss)
I0407 12:37:51.983387 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000516618 (* 0.0454545 = 2.34827e-05 loss)
I0407 12:37:51.983402 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000508504 (* 0.0454545 = 2.31138e-05 loss)
I0407 12:37:51.983430 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000507597 (* 0.0454545 = 2.30726e-05 loss)
I0407 12:37:51.983445 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.00050904 (* 0.0454545 = 2.31382e-05 loss)
I0407 12:37:51.983459 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000511597 (* 0.0454545 = 2.32544e-05 loss)
I0407 12:37:51.983474 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000516407 (* 0.0454545 = 2.3473e-05 loss)
I0407 12:37:51.983487 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000518495 (* 0.0454545 = 2.3568e-05 loss)
I0407 12:37:51.983500 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:37:51.983510 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.63321e-07
I0407 12:37:51.983527 32304 sgd_solver.cpp:106] Iteration 6500, lr = 0.00987
I0407 12:39:04.537638 32304 solver.cpp:229] Iteration 7000, loss = 1.15549
I0407 12:39:04.537758 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 12:39:04.537778 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 12:39:04.537791 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 12:39:04.537804 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0
I0407 12:39:04.537816 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 12:39:04.537828 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.46875
I0407 12:39:04.537839 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 12:39:04.537852 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 12:39:04.537863 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:39:04.537874 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 12:39:04.537886 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:39:04.537897 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:39:04.537909 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:39:04.537920 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:39:04.537932 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:39:04.537943 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:39:04.537955 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:39:04.537966 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:39:04.537977 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:39:04.537988 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:39:04.538000 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:39:04.538012 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:39:04.538028 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.79878 (* 0.0454545 = 0.172672 loss)
I0407 12:39:04.538041 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.13976 (* 0.0454545 = 0.188171 loss)
I0407 12:39:04.538055 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.10121 (* 0.0454545 = 0.186419 loss)
I0407 12:39:04.538069 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 4.09522 (* 0.0454545 = 0.186146 loss)
I0407 12:39:04.538086 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.61822 (* 0.0454545 = 0.164464 loss)
I0407 12:39:04.538100 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.74574 (* 0.0454545 = 0.124806 loss)
I0407 12:39:04.538115 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.41164 (* 0.0454545 = 0.0641654 loss)
I0407 12:39:04.538128 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.389344 (* 0.0454545 = 0.0176975 loss)
I0407 12:39:04.538142 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.330339 (* 0.0454545 = 0.0150154 loss)
I0407 12:39:04.538156 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.246965 (* 0.0454545 = 0.0112257 loss)
I0407 12:39:04.538171 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000303094 (* 0.0454545 = 1.3777e-05 loss)
I0407 12:39:04.538184 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000303327 (* 0.0454545 = 1.37876e-05 loss)
I0407 12:39:04.538202 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000313641 (* 0.0454545 = 1.42564e-05 loss)
I0407 12:39:04.538216 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00031227 (* 0.0454545 = 1.41941e-05 loss)
I0407 12:39:04.538230 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000309135 (* 0.0454545 = 1.40516e-05 loss)
I0407 12:39:04.538244 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000305636 (* 0.0454545 = 1.38925e-05 loss)
I0407 12:39:04.538259 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000298547 (* 0.0454545 = 1.35703e-05 loss)
I0407 12:39:04.538290 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000311424 (* 0.0454545 = 1.41557e-05 loss)
I0407 12:39:04.538303 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000303264 (* 0.0454545 = 1.37847e-05 loss)
I0407 12:39:04.538317 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000313182 (* 0.0454545 = 1.42356e-05 loss)
I0407 12:39:04.538331 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000308335 (* 0.0454545 = 1.40152e-05 loss)
I0407 12:39:04.538346 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000313592 (* 0.0454545 = 1.42542e-05 loss)
I0407 12:39:04.538358 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:39:04.538369 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.50809e-07
I0407 12:39:04.538384 32304 sgd_solver.cpp:106] Iteration 7000, lr = 0.00986
I0407 12:40:17.110080 32304 solver.cpp:229] Iteration 7500, loss = 1.14889
I0407 12:40:17.110216 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:40:17.110237 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 12:40:17.110250 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:40:17.110262 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 12:40:17.110275 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 12:40:17.110286 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 12:40:17.110298 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 12:40:17.110311 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 12:40:17.110322 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 12:40:17.110333 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:40:17.110344 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:40:17.110357 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:40:17.110368 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:40:17.110381 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:40:17.110394 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:40:17.110404 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:40:17.110415 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:40:17.110427 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:40:17.110438 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:40:17.110450 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:40:17.110461 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:40:17.110473 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:40:17.110489 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.80748 (* 0.0454545 = 0.173067 loss)
I0407 12:40:17.110503 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.19831 (* 0.0454545 = 0.190832 loss)
I0407 12:40:17.110517 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.13764 (* 0.0454545 = 0.188075 loss)
I0407 12:40:17.110532 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.88762 (* 0.0454545 = 0.17671 loss)
I0407 12:40:17.110545 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.76201 (* 0.0454545 = 0.171001 loss)
I0407 12:40:17.110558 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.1556 (* 0.0454545 = 0.143437 loss)
I0407 12:40:17.110572 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.97984 (* 0.0454545 = 0.0899927 loss)
I0407 12:40:17.110586 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.407387 (* 0.0454545 = 0.0185176 loss)
I0407 12:40:17.110600 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0373078 (* 0.0454545 = 0.00169581 loss)
I0407 12:40:17.110615 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0165968 (* 0.0454545 = 0.000754399 loss)
I0407 12:40:17.110628 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000314402 (* 0.0454545 = 1.4291e-05 loss)
I0407 12:40:17.110642 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000317453 (* 0.0454545 = 1.44297e-05 loss)
I0407 12:40:17.110656 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000311356 (* 0.0454545 = 1.41525e-05 loss)
I0407 12:40:17.110671 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000315988 (* 0.0454545 = 1.43631e-05 loss)
I0407 12:40:17.110684 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000315313 (* 0.0454545 = 1.43324e-05 loss)
I0407 12:40:17.110697 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000317243 (* 0.0454545 = 1.44201e-05 loss)
I0407 12:40:17.110712 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000314914 (* 0.0454545 = 1.43143e-05 loss)
I0407 12:40:17.110743 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000313566 (* 0.0454545 = 1.4253e-05 loss)
I0407 12:40:17.110757 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000314607 (* 0.0454545 = 1.43003e-05 loss)
I0407 12:40:17.110771 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.00031399 (* 0.0454545 = 1.42723e-05 loss)
I0407 12:40:17.110785 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000321604 (* 0.0454545 = 1.46184e-05 loss)
I0407 12:40:17.110800 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000313587 (* 0.0454545 = 1.42539e-05 loss)
I0407 12:40:17.110812 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:40:17.110823 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.9779e-07
I0407 12:40:17.110838 32304 sgd_solver.cpp:106] Iteration 7500, lr = 0.00985
I0407 12:41:29.144441 32304 solver.cpp:229] Iteration 8000, loss = 1.15006
I0407 12:41:29.144589 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 12:41:29.144610 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:41:29.144623 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:41:29.144635 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:41:29.144647 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.09375
I0407 12:41:29.144659 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.25
I0407 12:41:29.144671 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 12:41:29.144683 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.78125
I0407 12:41:29.144695 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.875
I0407 12:41:29.144706 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 12:41:29.144718 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:41:29.144729 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:41:29.144742 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:41:29.144752 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:41:29.144764 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:41:29.144775 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:41:29.144788 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:41:29.144798 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:41:29.144810 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:41:29.144821 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:41:29.144832 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:41:29.144845 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:41:29.144860 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.81065 (* 0.0454545 = 0.173211 loss)
I0407 12:41:29.144875 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.0179 (* 0.0454545 = 0.182632 loss)
I0407 12:41:29.144888 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.97328 (* 0.0454545 = 0.180604 loss)
I0407 12:41:29.144902 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.87517 (* 0.0454545 = 0.176144 loss)
I0407 12:41:29.144915 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 4.05054 (* 0.0454545 = 0.184116 loss)
I0407 12:41:29.144932 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.4431 (* 0.0454545 = 0.156505 loss)
I0407 12:41:29.144947 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.31716 (* 0.0454545 = 0.105326 loss)
I0407 12:41:29.144960 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.24544 (* 0.0454545 = 0.0566108 loss)
I0407 12:41:29.144974 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.811652 (* 0.0454545 = 0.0368933 loss)
I0407 12:41:29.144987 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.418769 (* 0.0454545 = 0.019035 loss)
I0407 12:41:29.145002 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000697765 (* 0.0454545 = 3.17166e-05 loss)
I0407 12:41:29.145016 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00068914 (* 0.0454545 = 3.13246e-05 loss)
I0407 12:41:29.145030 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000697888 (* 0.0454545 = 3.17222e-05 loss)
I0407 12:41:29.145043 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000689626 (* 0.0454545 = 3.13466e-05 loss)
I0407 12:41:29.145057 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000701506 (* 0.0454545 = 3.18866e-05 loss)
I0407 12:41:29.145071 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000687836 (* 0.0454545 = 3.12653e-05 loss)
I0407 12:41:29.145086 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000692998 (* 0.0454545 = 3.14999e-05 loss)
I0407 12:41:29.145115 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.00068982 (* 0.0454545 = 3.13555e-05 loss)
I0407 12:41:29.145131 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000694798 (* 0.0454545 = 3.15817e-05 loss)
I0407 12:41:29.145145 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000693987 (* 0.0454545 = 3.15449e-05 loss)
I0407 12:41:29.145159 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.00068946 (* 0.0454545 = 3.13391e-05 loss)
I0407 12:41:29.145174 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000688382 (* 0.0454545 = 3.12901e-05 loss)
I0407 12:41:29.145185 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:41:29.145197 32304 solver.cpp:245]     Train net output #45: total_confidence = 1.86273e-07
I0407 12:41:29.145211 32304 sgd_solver.cpp:106] Iteration 8000, lr = 0.00984
I0407 12:42:42.605814 32304 solver.cpp:229] Iteration 8500, loss = 1.14496
I0407 12:42:42.605962 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:42:42.605984 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 12:42:42.605998 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:42:42.606009 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 12:42:42.606022 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 12:42:42.606034 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 12:42:42.606046 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 12:42:42.606057 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 12:42:42.606070 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 12:42:42.606084 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 12:42:42.606097 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:42:42.606108 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:42:42.606122 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:42:42.606132 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:42:42.606144 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:42:42.606155 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:42:42.606166 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:42:42.606178 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:42:42.606189 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:42:42.606201 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:42:42.606212 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:42:42.606225 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:42:42.606240 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.80991 (* 0.0454545 = 0.173178 loss)
I0407 12:42:42.606254 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.91702 (* 0.0454545 = 0.178047 loss)
I0407 12:42:42.606268 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.11918 (* 0.0454545 = 0.187235 loss)
I0407 12:42:42.606282 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.89953 (* 0.0454545 = 0.177251 loss)
I0407 12:42:42.606297 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.61506 (* 0.0454545 = 0.164321 loss)
I0407 12:42:42.606309 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.2225 (* 0.0454545 = 0.146477 loss)
I0407 12:42:42.606323 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.28889 (* 0.0454545 = 0.10404 loss)
I0407 12:42:42.606338 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.946521 (* 0.0454545 = 0.0430237 loss)
I0407 12:42:42.606351 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.398307 (* 0.0454545 = 0.0181049 loss)
I0407 12:42:42.606365 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.237103 (* 0.0454545 = 0.0107774 loss)
I0407 12:42:42.606379 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000250063 (* 0.0454545 = 1.13665e-05 loss)
I0407 12:42:42.606394 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00024798 (* 0.0454545 = 1.12718e-05 loss)
I0407 12:42:42.606407 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000251866 (* 0.0454545 = 1.14485e-05 loss)
I0407 12:42:42.606421 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000248801 (* 0.0454545 = 1.13091e-05 loss)
I0407 12:42:42.606436 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000251637 (* 0.0454545 = 1.1438e-05 loss)
I0407 12:42:42.606449 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000247645 (* 0.0454545 = 1.12566e-05 loss)
I0407 12:42:42.606463 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000248377 (* 0.0454545 = 1.12899e-05 loss)
I0407 12:42:42.606492 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000252392 (* 0.0454545 = 1.14723e-05 loss)
I0407 12:42:42.606525 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000253594 (* 0.0454545 = 1.1527e-05 loss)
I0407 12:42:42.606541 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000247073 (* 0.0454545 = 1.12306e-05 loss)
I0407 12:42:42.606555 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000250983 (* 0.0454545 = 1.14083e-05 loss)
I0407 12:42:42.606570 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000244505 (* 0.0454545 = 1.11139e-05 loss)
I0407 12:42:42.606581 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:42:42.606593 32304 solver.cpp:245]     Train net output #45: total_confidence = 4.65583e-07
I0407 12:42:42.606607 32304 sgd_solver.cpp:106] Iteration 8500, lr = 0.00983
I0407 12:43:54.874897 32304 solver.cpp:229] Iteration 9000, loss = 1.15044
I0407 12:43:54.875046 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0
I0407 12:43:54.875066 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 12:43:54.875079 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 12:43:54.875092 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:43:54.875103 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 12:43:54.875115 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 12:43:54.875128 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 12:43:54.875138 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 12:43:54.875159 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:43:54.875170 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 12:43:54.875181 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:43:54.875193 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:43:54.875205 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:43:54.875217 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:43:54.875236 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:43:54.875247 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:43:54.875257 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:43:54.875269 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:43:54.875280 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:43:54.875291 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:43:54.875303 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:43:54.875313 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:43:54.875344 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.95831 (* 0.0454545 = 0.179923 loss)
I0407 12:43:54.875360 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.86887 (* 0.0454545 = 0.175858 loss)
I0407 12:43:54.875375 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.97132 (* 0.0454545 = 0.180514 loss)
I0407 12:43:54.875388 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 4.0901 (* 0.0454545 = 0.185914 loss)
I0407 12:43:54.875401 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.63569 (* 0.0454545 = 0.165259 loss)
I0407 12:43:54.875416 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.43806 (* 0.0454545 = 0.156275 loss)
I0407 12:43:54.875429 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.53019 (* 0.0454545 = 0.0695542 loss)
I0407 12:43:54.875442 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.45009 (* 0.0454545 = 0.0204586 loss)
I0407 12:43:54.875457 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.234797 (* 0.0454545 = 0.0106726 loss)
I0407 12:43:54.875469 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.231475 (* 0.0454545 = 0.0105216 loss)
I0407 12:43:54.875484 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000439177 (* 0.0454545 = 1.99626e-05 loss)
I0407 12:43:54.875499 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000428826 (* 0.0454545 = 1.94921e-05 loss)
I0407 12:43:54.875512 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000428384 (* 0.0454545 = 1.9472e-05 loss)
I0407 12:43:54.875526 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000427731 (* 0.0454545 = 1.94423e-05 loss)
I0407 12:43:54.875540 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.00043877 (* 0.0454545 = 1.99441e-05 loss)
I0407 12:43:54.875553 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000441436 (* 0.0454545 = 2.00653e-05 loss)
I0407 12:43:54.875567 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000438306 (* 0.0454545 = 1.9923e-05 loss)
I0407 12:43:54.875600 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000436142 (* 0.0454545 = 1.98247e-05 loss)
I0407 12:43:54.875615 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000433418 (* 0.0454545 = 1.97008e-05 loss)
I0407 12:43:54.875629 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000431682 (* 0.0454545 = 1.96219e-05 loss)
I0407 12:43:54.875643 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000436207 (* 0.0454545 = 1.98276e-05 loss)
I0407 12:43:54.875656 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000426104 (* 0.0454545 = 1.93684e-05 loss)
I0407 12:43:54.875669 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:43:54.875680 32304 solver.cpp:245]     Train net output #45: total_confidence = 1.90111e-07
I0407 12:43:54.875695 32304 sgd_solver.cpp:106] Iteration 9000, lr = 0.00982
I0407 12:45:07.056236 32304 solver.cpp:229] Iteration 9500, loss = 1.14678
I0407 12:45:07.056432 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 12:45:07.056453 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 12:45:07.056466 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 12:45:07.056478 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:45:07.056490 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 12:45:07.056502 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.28125
I0407 12:45:07.056514 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 12:45:07.056526 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 12:45:07.056538 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.875
I0407 12:45:07.056550 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.90625
I0407 12:45:07.056565 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:45:07.056577 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:45:07.056589 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:45:07.056601 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:45:07.056612 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:45:07.056623 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:45:07.056634 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:45:07.056645 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:45:07.056665 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:45:07.056676 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:45:07.056689 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:45:07.056699 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:45:07.056715 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.72311 (* 0.0454545 = 0.169232 loss)
I0407 12:45:07.056738 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.94429 (* 0.0454545 = 0.179286 loss)
I0407 12:45:07.056752 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.85895 (* 0.0454545 = 0.175407 loss)
I0407 12:45:07.056766 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 4.07724 (* 0.0454545 = 0.185329 loss)
I0407 12:45:07.056779 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.55533 (* 0.0454545 = 0.161606 loss)
I0407 12:45:07.056792 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.20156 (* 0.0454545 = 0.145526 loss)
I0407 12:45:07.056807 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.20264 (* 0.0454545 = 0.10012 loss)
I0407 12:45:07.056819 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.854239 (* 0.0454545 = 0.038829 loss)
I0407 12:45:07.056833 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.753499 (* 0.0454545 = 0.03425 loss)
I0407 12:45:07.056848 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.650582 (* 0.0454545 = 0.0295719 loss)
I0407 12:45:07.056862 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000687669 (* 0.0454545 = 3.12577e-05 loss)
I0407 12:45:07.056876 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000679532 (* 0.0454545 = 3.08878e-05 loss)
I0407 12:45:07.056890 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000663475 (* 0.0454545 = 3.0158e-05 loss)
I0407 12:45:07.056905 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000668629 (* 0.0454545 = 3.03922e-05 loss)
I0407 12:45:07.056922 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.0006551 (* 0.0454545 = 2.97773e-05 loss)
I0407 12:45:07.056937 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000652968 (* 0.0454545 = 2.96804e-05 loss)
I0407 12:45:07.056951 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000639437 (* 0.0454545 = 2.90653e-05 loss)
I0407 12:45:07.062273 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000655921 (* 0.0454545 = 2.98146e-05 loss)
I0407 12:45:07.062297 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000662369 (* 0.0454545 = 3.01077e-05 loss)
I0407 12:45:07.062317 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000655812 (* 0.0454545 = 2.98096e-05 loss)
I0407 12:45:07.062331 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000650431 (* 0.0454545 = 2.9565e-05 loss)
I0407 12:45:07.062345 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000649896 (* 0.0454545 = 2.95407e-05 loss)
I0407 12:45:07.062358 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:45:07.062369 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.4749e-07
I0407 12:45:07.062384 32304 sgd_solver.cpp:106] Iteration 9500, lr = 0.00981
I0407 12:46:19.240777 32304 solver.cpp:338] Iteration 10000, Testing net (#0)
I0407 12:46:27.311863 32304 solver.cpp:393] Test loss: 1.02304
I0407 12:46:27.311929 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.002
I0407 12:46:27.311947 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.094
I0407 12:46:27.311959 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.068
I0407 12:46:27.311971 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.07
I0407 12:46:27.311983 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.213
I0407 12:46:27.311995 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.502
I0407 12:46:27.312006 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.894
I0407 12:46:27.312018 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 12:46:27.312029 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 12:46:27.312041 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 12:46:27.312052 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 12:46:27.312063 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 12:46:27.312075 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 12:46:27.312086 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 12:46:27.312098 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 12:46:27.312108 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 12:46:27.312120 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 12:46:27.312131 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 12:46:27.312142 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 12:46:27.312153 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 12:46:27.312165 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 12:46:27.312175 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 12:46:27.312191 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 3.39182 (* 0.0454545 = 0.154173 loss)
I0407 12:46:27.312206 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.71564 (* 0.0454545 = 0.168893 loss)
I0407 12:46:27.312218 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 3.93239 (* 0.0454545 = 0.178745 loss)
I0407 12:46:27.312232 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 3.82176 (* 0.0454545 = 0.173716 loss)
I0407 12:46:27.312245 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 3.78885 (* 0.0454545 = 0.17222 loss)
I0407 12:46:27.312259 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 2.54097 (* 0.0454545 = 0.115499 loss)
I0407 12:46:27.312273 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.893392 (* 0.0454545 = 0.0406087 loss)
I0407 12:46:27.312285 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.308662 (* 0.0454545 = 0.0140301 loss)
I0407 12:46:27.312299 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0755589 (* 0.0454545 = 0.00343449 loss)
I0407 12:46:27.312314 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0343514 (* 0.0454545 = 0.00156143 loss)
I0407 12:46:27.312327 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 0.000301931 (* 0.0454545 = 1.37241e-05 loss)
I0407 12:46:27.312341 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 0.000302494 (* 0.0454545 = 1.37497e-05 loss)
I0407 12:46:27.312355 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 0.000301247 (* 0.0454545 = 1.36931e-05 loss)
I0407 12:46:27.312368 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 0.000296181 (* 0.0454545 = 1.34628e-05 loss)
I0407 12:46:27.312382 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 0.000295907 (* 0.0454545 = 1.34503e-05 loss)
I0407 12:46:27.312397 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 0.000297148 (* 0.0454545 = 1.35067e-05 loss)
I0407 12:46:27.312409 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 0.0002927 (* 0.0454545 = 1.33046e-05 loss)
I0407 12:46:27.312459 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 0.000295707 (* 0.0454545 = 1.34412e-05 loss)
I0407 12:46:27.312474 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 0.000295452 (* 0.0454545 = 1.34296e-05 loss)
I0407 12:46:27.312487 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 0.00029803 (* 0.0454545 = 1.35468e-05 loss)
I0407 12:46:27.312501 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 0.000295397 (* 0.0454545 = 1.34271e-05 loss)
I0407 12:46:27.312515 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 0.000295115 (* 0.0454545 = 1.34143e-05 loss)
I0407 12:46:27.312527 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0
I0407 12:46:27.312538 32304 solver.cpp:406]     Test net output #45: total_confidence = 3.85436e-07
I0407 12:46:27.347630 32304 solver.cpp:229] Iteration 10000, loss = 1.14363
I0407 12:46:27.347683 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:46:27.347703 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:46:27.347718 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:46:27.347729 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:46:27.347741 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 12:46:27.347754 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 12:46:27.347765 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 12:46:27.347777 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 12:46:27.347790 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 12:46:27.347800 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 12:46:27.347812 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:46:27.347825 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:46:27.347836 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:46:27.347847 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:46:27.347858 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:46:27.347870 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:46:27.347882 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:46:27.347893 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:46:27.347904 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:46:27.347915 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:46:27.347928 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:46:27.347939 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:46:27.347954 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.88658 (* 0.0454545 = 0.176663 loss)
I0407 12:46:27.347968 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.97577 (* 0.0454545 = 0.180717 loss)
I0407 12:46:27.347981 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.88081 (* 0.0454545 = 0.1764 loss)
I0407 12:46:27.347995 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.97871 (* 0.0454545 = 0.18085 loss)
I0407 12:46:27.348009 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.83197 (* 0.0454545 = 0.17418 loss)
I0407 12:46:27.348023 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.40787 (* 0.0454545 = 0.154903 loss)
I0407 12:46:27.348037 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.65345 (* 0.0454545 = 0.0751568 loss)
I0407 12:46:27.348050 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.700909 (* 0.0454545 = 0.0318595 loss)
I0407 12:46:27.348063 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.522103 (* 0.0454545 = 0.0237319 loss)
I0407 12:46:27.348080 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.247131 (* 0.0454545 = 0.0112332 loss)
I0407 12:46:27.348121 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000314942 (* 0.0454545 = 1.43156e-05 loss)
I0407 12:46:27.348137 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000319644 (* 0.0454545 = 1.45293e-05 loss)
I0407 12:46:27.348151 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000312148 (* 0.0454545 = 1.41886e-05 loss)
I0407 12:46:27.348165 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000303596 (* 0.0454545 = 1.37998e-05 loss)
I0407 12:46:27.348179 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000308778 (* 0.0454545 = 1.40354e-05 loss)
I0407 12:46:27.348193 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000305129 (* 0.0454545 = 1.38695e-05 loss)
I0407 12:46:27.348207 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000296677 (* 0.0454545 = 1.34853e-05 loss)
I0407 12:46:27.348222 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000300678 (* 0.0454545 = 1.36672e-05 loss)
I0407 12:46:27.348235 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.00030581 (* 0.0454545 = 1.39004e-05 loss)
I0407 12:46:27.348249 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.0003076 (* 0.0454545 = 1.39818e-05 loss)
I0407 12:46:27.348263 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000301053 (* 0.0454545 = 1.36842e-05 loss)
I0407 12:46:27.348278 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000301231 (* 0.0454545 = 1.36923e-05 loss)
I0407 12:46:27.348289 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:46:27.348300 32304 solver.cpp:245]     Train net output #45: total_confidence = 3.87051e-07
I0407 12:46:27.348315 32304 sgd_solver.cpp:106] Iteration 10000, lr = 0.0098
I0407 12:47:39.401563 32304 solver.cpp:229] Iteration 10500, loss = 1.13767
I0407 12:47:39.401710 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:47:39.401731 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 12:47:39.401744 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 12:47:39.401757 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:47:39.401768 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 12:47:39.401780 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 12:47:39.401793 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 12:47:39.401804 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 12:47:39.401816 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 12:47:39.401829 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:47:39.401840 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:47:39.401852 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:47:39.401864 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:47:39.401875 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:47:39.401886 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:47:39.401897 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:47:39.401909 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:47:39.401923 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:47:39.401935 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:47:39.401947 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:47:39.401958 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:47:39.401969 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:47:39.401985 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.86765 (* 0.0454545 = 0.175802 loss)
I0407 12:47:39.401999 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.98933 (* 0.0454545 = 0.181333 loss)
I0407 12:47:39.402012 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.01847 (* 0.0454545 = 0.182658 loss)
I0407 12:47:39.402026 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 4.01197 (* 0.0454545 = 0.182362 loss)
I0407 12:47:39.402040 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.83496 (* 0.0454545 = 0.174316 loss)
I0407 12:47:39.402053 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.14108 (* 0.0454545 = 0.142776 loss)
I0407 12:47:39.402067 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.84664 (* 0.0454545 = 0.0839383 loss)
I0407 12:47:39.402081 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.793755 (* 0.0454545 = 0.0360798 loss)
I0407 12:47:39.402094 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.619149 (* 0.0454545 = 0.0281431 loss)
I0407 12:47:39.402107 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00819994 (* 0.0454545 = 0.000372724 loss)
I0407 12:47:39.402122 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 7.02942e-05 (* 0.0454545 = 3.19519e-06 loss)
I0407 12:47:39.402135 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.17249e-05 (* 0.0454545 = 3.26022e-06 loss)
I0407 12:47:39.402149 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.03108e-05 (* 0.0454545 = 3.19595e-06 loss)
I0407 12:47:39.402163 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.81234e-05 (* 0.0454545 = 3.09652e-06 loss)
I0407 12:47:39.402178 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 6.95492e-05 (* 0.0454545 = 3.16133e-06 loss)
I0407 12:47:39.402190 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.94988e-05 (* 0.0454545 = 3.15903e-06 loss)
I0407 12:47:39.402204 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.8977e-05 (* 0.0454545 = 3.13532e-06 loss)
I0407 12:47:39.402235 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 6.84387e-05 (* 0.0454545 = 3.11085e-06 loss)
I0407 12:47:39.402250 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.93628e-05 (* 0.0454545 = 3.15286e-06 loss)
I0407 12:47:39.402263 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.06558e-05 (* 0.0454545 = 3.21163e-06 loss)
I0407 12:47:39.402277 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.94669e-05 (* 0.0454545 = 3.15758e-06 loss)
I0407 12:47:39.402292 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.9739e-05 (* 0.0454545 = 3.16996e-06 loss)
I0407 12:47:39.402303 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:47:39.402314 32304 solver.cpp:245]     Train net output #45: total_confidence = 4.6848e-07
I0407 12:47:39.402329 32304 sgd_solver.cpp:106] Iteration 10500, lr = 0.00979
I0407 12:48:53.763520 32304 solver.cpp:229] Iteration 11000, loss = 1.14074
I0407 12:48:53.763665 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 12:48:53.763685 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 12:48:53.763698 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:48:53.763710 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 12:48:53.763722 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 12:48:53.763734 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 12:48:53.763746 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 12:48:53.763757 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 12:48:53.763769 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:48:53.763782 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:48:53.763792 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:48:53.763803 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:48:53.763815 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:48:53.763828 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:48:53.763839 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:48:53.763850 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:48:53.763861 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:48:53.763872 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:48:53.763883 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:48:53.763895 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:48:53.763906 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:48:53.763919 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:48:53.763936 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.6994 (* 0.0454545 = 0.168155 loss)
I0407 12:48:53.763950 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.98261 (* 0.0454545 = 0.181028 loss)
I0407 12:48:53.763964 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.87028 (* 0.0454545 = 0.175922 loss)
I0407 12:48:53.763978 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.86304 (* 0.0454545 = 0.175593 loss)
I0407 12:48:53.763991 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.73816 (* 0.0454545 = 0.169917 loss)
I0407 12:48:53.764004 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.3573 (* 0.0454545 = 0.152605 loss)
I0407 12:48:53.764019 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.99769 (* 0.0454545 = 0.0908042 loss)
I0407 12:48:53.764031 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.837797 (* 0.0454545 = 0.0380817 loss)
I0407 12:48:53.764045 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.241517 (* 0.0454545 = 0.010978 loss)
I0407 12:48:53.764060 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0210889 (* 0.0454545 = 0.000958588 loss)
I0407 12:48:53.764073 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000615445 (* 0.0454545 = 2.79748e-05 loss)
I0407 12:48:53.764087 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00061342 (* 0.0454545 = 2.78827e-05 loss)
I0407 12:48:53.764101 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.0006057 (* 0.0454545 = 2.75318e-05 loss)
I0407 12:48:53.764116 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000605447 (* 0.0454545 = 2.75203e-05 loss)
I0407 12:48:53.764128 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000606496 (* 0.0454545 = 2.7568e-05 loss)
I0407 12:48:53.764142 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000610184 (* 0.0454545 = 2.77356e-05 loss)
I0407 12:48:53.764156 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000579744 (* 0.0454545 = 2.6352e-05 loss)
I0407 12:48:53.764188 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000594763 (* 0.0454545 = 2.70347e-05 loss)
I0407 12:48:53.764201 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000586364 (* 0.0454545 = 2.66529e-05 loss)
I0407 12:48:53.764215 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000590947 (* 0.0454545 = 2.68612e-05 loss)
I0407 12:48:53.764230 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000587761 (* 0.0454545 = 2.67164e-05 loss)
I0407 12:48:53.764243 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000586336 (* 0.0454545 = 2.66516e-05 loss)
I0407 12:48:53.764255 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:48:53.764266 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.23949e-07
I0407 12:48:53.764281 32304 sgd_solver.cpp:106] Iteration 11000, lr = 0.00978
I0407 12:50:05.746598 32304 solver.cpp:229] Iteration 11500, loss = 1.13678
I0407 12:50:05.746736 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 12:50:05.746757 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 12:50:05.746769 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:50:05.746781 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 12:50:05.746794 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.40625
I0407 12:50:05.746806 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5
I0407 12:50:05.746819 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 12:50:05.746829 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 12:50:05.746841 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 12:50:05.746853 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:50:05.746865 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:50:05.746876 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:50:05.746888 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:50:05.746899 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:50:05.746911 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:50:05.746925 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:50:05.746937 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:50:05.746948 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:50:05.746960 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:50:05.746971 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:50:05.746983 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:50:05.746994 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:50:05.747010 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.76228 (* 0.0454545 = 0.171013 loss)
I0407 12:50:05.747025 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.8555 (* 0.0454545 = 0.17525 loss)
I0407 12:50:05.747040 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.92111 (* 0.0454545 = 0.178232 loss)
I0407 12:50:05.747053 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.85121 (* 0.0454545 = 0.175055 loss)
I0407 12:50:05.747066 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.13208 (* 0.0454545 = 0.142367 loss)
I0407 12:50:05.747081 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.436 (* 0.0454545 = 0.110727 loss)
I0407 12:50:05.747094 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.85264 (* 0.0454545 = 0.0842111 loss)
I0407 12:50:05.747108 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.853314 (* 0.0454545 = 0.038787 loss)
I0407 12:50:05.747123 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.454596 (* 0.0454545 = 0.0206635 loss)
I0407 12:50:05.747138 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0119632 (* 0.0454545 = 0.00054378 loss)
I0407 12:50:05.747165 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000165724 (* 0.0454545 = 7.5329e-06 loss)
I0407 12:50:05.747189 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000168268 (* 0.0454545 = 7.64855e-06 loss)
I0407 12:50:05.747205 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000165362 (* 0.0454545 = 7.51647e-06 loss)
I0407 12:50:05.747218 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000166201 (* 0.0454545 = 7.55461e-06 loss)
I0407 12:50:05.747232 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.00016779 (* 0.0454545 = 7.62683e-06 loss)
I0407 12:50:05.747246 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000162111 (* 0.0454545 = 7.36867e-06 loss)
I0407 12:50:05.747262 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000155133 (* 0.0454545 = 7.05151e-06 loss)
I0407 12:50:05.747293 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000160095 (* 0.0454545 = 7.27706e-06 loss)
I0407 12:50:05.747308 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.00016096 (* 0.0454545 = 7.31635e-06 loss)
I0407 12:50:05.747337 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000162468 (* 0.0454545 = 7.3849e-06 loss)
I0407 12:50:05.747354 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000161029 (* 0.0454545 = 7.31948e-06 loss)
I0407 12:50:05.747367 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.00016155 (* 0.0454545 = 7.34318e-06 loss)
I0407 12:50:05.747380 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:50:05.747391 32304 solver.cpp:245]     Train net output #45: total_confidence = 5.81777e-07
I0407 12:50:05.747406 32304 sgd_solver.cpp:106] Iteration 11500, lr = 0.00977
I0407 12:51:17.512676 32304 solver.cpp:229] Iteration 12000, loss = 1.13521
I0407 12:51:17.512845 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.21875
I0407 12:51:17.512866 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 12:51:17.512878 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:51:17.512890 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:51:17.512902 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 12:51:17.512914 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 12:51:17.512929 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.5625
I0407 12:51:17.512941 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.78125
I0407 12:51:17.512953 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 12:51:17.512964 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 12:51:17.512975 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:51:17.512987 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:51:17.513000 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:51:17.513010 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:51:17.513022 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:51:17.513033 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:51:17.513044 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:51:17.513056 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:51:17.513067 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:51:17.513078 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:51:17.513089 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:51:17.513101 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:51:17.513116 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.66973 (* 0.0454545 = 0.166806 loss)
I0407 12:51:17.513131 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.89817 (* 0.0454545 = 0.177189 loss)
I0407 12:51:17.513145 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.82759 (* 0.0454545 = 0.173982 loss)
I0407 12:51:17.513159 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.8895 (* 0.0454545 = 0.176795 loss)
I0407 12:51:17.513172 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.63073 (* 0.0454545 = 0.165033 loss)
I0407 12:51:17.513185 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.10668 (* 0.0454545 = 0.141213 loss)
I0407 12:51:17.513200 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.56309 (* 0.0454545 = 0.116504 loss)
I0407 12:51:17.513212 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.10902 (* 0.0454545 = 0.0504102 loss)
I0407 12:51:17.513226 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.406374 (* 0.0454545 = 0.0184715 loss)
I0407 12:51:17.513241 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.212828 (* 0.0454545 = 0.00967398 loss)
I0407 12:51:17.513254 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000569602 (* 0.0454545 = 2.5891e-05 loss)
I0407 12:51:17.513269 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00057588 (* 0.0454545 = 2.61764e-05 loss)
I0407 12:51:17.513283 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000558275 (* 0.0454545 = 2.53762e-05 loss)
I0407 12:51:17.513298 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000559878 (* 0.0454545 = 2.5449e-05 loss)
I0407 12:51:17.513311 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000566423 (* 0.0454545 = 2.57465e-05 loss)
I0407 12:51:17.513325 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.0005562 (* 0.0454545 = 2.52818e-05 loss)
I0407 12:51:17.513339 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000558121 (* 0.0454545 = 2.53691e-05 loss)
I0407 12:51:17.513365 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000549762 (* 0.0454545 = 2.49892e-05 loss)
I0407 12:51:17.513381 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000553909 (* 0.0454545 = 2.51777e-05 loss)
I0407 12:51:17.513394 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000557469 (* 0.0454545 = 2.53395e-05 loss)
I0407 12:51:17.513408 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000548548 (* 0.0454545 = 2.4934e-05 loss)
I0407 12:51:17.513422 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000547689 (* 0.0454545 = 2.4895e-05 loss)
I0407 12:51:17.513433 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:51:17.513445 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.19033e-07
I0407 12:51:17.513460 32304 sgd_solver.cpp:106] Iteration 12000, lr = 0.00976
I0407 12:52:28.828668 32304 solver.cpp:229] Iteration 12500, loss = 1.13642
I0407 12:52:28.828804 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:52:28.828824 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:52:28.828836 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:52:28.828848 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:52:28.828860 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 12:52:28.828872 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 12:52:28.828883 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 12:52:28.828896 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.78125
I0407 12:52:28.828907 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.84375
I0407 12:52:28.828922 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 12:52:28.828934 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:52:28.828945 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:52:28.828956 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:52:28.828968 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:52:28.828979 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:52:28.828990 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:52:28.829001 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:52:28.829012 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:52:28.829023 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:52:28.829035 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:52:28.829046 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:52:28.829057 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:52:28.829073 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.92166 (* 0.0454545 = 0.178257 loss)
I0407 12:52:28.829087 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.8468 (* 0.0454545 = 0.174854 loss)
I0407 12:52:28.829102 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.94098 (* 0.0454545 = 0.179135 loss)
I0407 12:52:28.829121 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.98817 (* 0.0454545 = 0.181281 loss)
I0407 12:52:28.829146 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.8659 (* 0.0454545 = 0.175723 loss)
I0407 12:52:28.829162 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.11866 (* 0.0454545 = 0.141757 loss)
I0407 12:52:28.829192 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.60451 (* 0.0454545 = 0.0729324 loss)
I0407 12:52:28.829216 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.24991 (* 0.0454545 = 0.0568142 loss)
I0407 12:52:28.829231 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.966181 (* 0.0454545 = 0.0439173 loss)
I0407 12:52:28.829244 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.397371 (* 0.0454545 = 0.0180623 loss)
I0407 12:52:28.829262 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000669788 (* 0.0454545 = 3.04449e-05 loss)
I0407 12:52:28.829277 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000675009 (* 0.0454545 = 3.06822e-05 loss)
I0407 12:52:28.829291 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000655578 (* 0.0454545 = 2.9799e-05 loss)
I0407 12:52:28.829313 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00065918 (* 0.0454545 = 2.99627e-05 loss)
I0407 12:52:28.829327 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000653914 (* 0.0454545 = 2.97234e-05 loss)
I0407 12:52:28.829341 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000638033 (* 0.0454545 = 2.90015e-05 loss)
I0407 12:52:28.829355 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000639912 (* 0.0454545 = 2.90869e-05 loss)
I0407 12:52:28.829387 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000650657 (* 0.0454545 = 2.95753e-05 loss)
I0407 12:52:28.829402 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000629187 (* 0.0454545 = 2.85994e-05 loss)
I0407 12:52:28.829416 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000653489 (* 0.0454545 = 2.9704e-05 loss)
I0407 12:52:28.829430 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.00065289 (* 0.0454545 = 2.96768e-05 loss)
I0407 12:52:28.829453 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000653695 (* 0.0454545 = 2.97134e-05 loss)
I0407 12:52:28.829465 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:52:28.829483 32304 solver.cpp:245]     Train net output #45: total_confidence = 3.35254e-07
I0407 12:52:28.829516 32304 sgd_solver.cpp:106] Iteration 12500, lr = 0.00975
I0407 12:53:41.075163 32304 solver.cpp:229] Iteration 13000, loss = 1.12854
I0407 12:53:41.075307 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.15625
I0407 12:53:41.075327 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:53:41.075340 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:53:41.075353 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:53:41.075366 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 12:53:41.075377 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 12:53:41.075388 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 12:53:41.075400 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 12:53:41.075412 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:53:41.075424 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 12:53:41.075435 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:53:41.075448 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:53:41.075459 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:53:41.075470 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:53:41.075482 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:53:41.075495 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:53:41.075505 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:53:41.075516 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:53:41.075528 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:53:41.075539 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:53:41.075551 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:53:41.075562 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:53:41.075578 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.76399 (* 0.0454545 = 0.17109 loss)
I0407 12:53:41.075592 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.01692 (* 0.0454545 = 0.182587 loss)
I0407 12:53:41.075606 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 4.0409 (* 0.0454545 = 0.183677 loss)
I0407 12:53:41.075619 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.86731 (* 0.0454545 = 0.175787 loss)
I0407 12:53:41.075634 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.73454 (* 0.0454545 = 0.169752 loss)
I0407 12:53:41.075647 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.9815 (* 0.0454545 = 0.135523 loss)
I0407 12:53:41.075660 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.68755 (* 0.0454545 = 0.0767067 loss)
I0407 12:53:41.075675 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.725003 (* 0.0454545 = 0.0329547 loss)
I0407 12:53:41.075688 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.236804 (* 0.0454545 = 0.0107638 loss)
I0407 12:53:41.075701 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.242391 (* 0.0454545 = 0.0110178 loss)
I0407 12:53:41.075716 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000410011 (* 0.0454545 = 1.86369e-05 loss)
I0407 12:53:41.075729 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000410588 (* 0.0454545 = 1.86631e-05 loss)
I0407 12:53:41.075743 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000413714 (* 0.0454545 = 1.88052e-05 loss)
I0407 12:53:41.075757 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00039744 (* 0.0454545 = 1.80655e-05 loss)
I0407 12:53:41.075772 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000405761 (* 0.0454545 = 1.84437e-05 loss)
I0407 12:53:41.075785 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000403384 (* 0.0454545 = 1.83356e-05 loss)
I0407 12:53:41.075798 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000394309 (* 0.0454545 = 1.79231e-05 loss)
I0407 12:53:41.075834 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000401757 (* 0.0454545 = 1.82617e-05 loss)
I0407 12:53:41.075863 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000393122 (* 0.0454545 = 1.78692e-05 loss)
I0407 12:53:41.075882 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000403367 (* 0.0454545 = 1.83349e-05 loss)
I0407 12:53:41.075896 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.00039621 (* 0.0454545 = 1.80096e-05 loss)
I0407 12:53:41.075911 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000404288 (* 0.0454545 = 1.83767e-05 loss)
I0407 12:53:41.075927 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:53:41.075938 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.91587e-07
I0407 12:53:41.075953 32304 sgd_solver.cpp:106] Iteration 13000, lr = 0.00974
I0407 12:54:52.999969 32304 solver.cpp:229] Iteration 13500, loss = 1.13014
I0407 12:54:53.000134 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 12:54:53.000155 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 12:54:53.000169 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 12:54:53.000181 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 12:54:53.000193 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 12:54:53.000205 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5625
I0407 12:54:53.000216 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.90625
I0407 12:54:53.000228 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 12:54:53.000241 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:54:53.000252 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:54:53.000262 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:54:53.000274 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:54:53.000290 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:54:53.000315 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:54:53.000331 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:54:53.000342 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:54:53.000355 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:54:53.000365 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:54:53.000376 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:54:53.000388 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:54:53.000401 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:54:53.000411 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:54:53.000427 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.88831 (* 0.0454545 = 0.176742 loss)
I0407 12:54:53.000442 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.92207 (* 0.0454545 = 0.178276 loss)
I0407 12:54:53.000457 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.98251 (* 0.0454545 = 0.181023 loss)
I0407 12:54:53.000469 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.86811 (* 0.0454545 = 0.175823 loss)
I0407 12:54:53.000483 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.67674 (* 0.0454545 = 0.167125 loss)
I0407 12:54:53.000496 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.4084 (* 0.0454545 = 0.109473 loss)
I0407 12:54:53.000510 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.812194 (* 0.0454545 = 0.0369179 loss)
I0407 12:54:53.000524 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.247532 (* 0.0454545 = 0.0112514 loss)
I0407 12:54:53.000538 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.190389 (* 0.0454545 = 0.00865405 loss)
I0407 12:54:53.000551 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0196044 (* 0.0454545 = 0.000891111 loss)
I0407 12:54:53.000566 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000444755 (* 0.0454545 = 2.02161e-05 loss)
I0407 12:54:53.000579 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000449766 (* 0.0454545 = 2.04439e-05 loss)
I0407 12:54:53.000593 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000432993 (* 0.0454545 = 1.96815e-05 loss)
I0407 12:54:53.000607 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00043181 (* 0.0454545 = 1.96277e-05 loss)
I0407 12:54:53.000620 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.00042766 (* 0.0454545 = 1.94391e-05 loss)
I0407 12:54:53.000635 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000427163 (* 0.0454545 = 1.94165e-05 loss)
I0407 12:54:53.000648 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.00043617 (* 0.0454545 = 1.98259e-05 loss)
I0407 12:54:53.000676 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000424108 (* 0.0454545 = 1.92776e-05 loss)
I0407 12:54:53.000691 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000426783 (* 0.0454545 = 1.93992e-05 loss)
I0407 12:54:53.000705 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000428324 (* 0.0454545 = 1.94693e-05 loss)
I0407 12:54:53.000723 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000422863 (* 0.0454545 = 1.9221e-05 loss)
I0407 12:54:53.000738 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000414585 (* 0.0454545 = 1.88448e-05 loss)
I0407 12:54:53.000751 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:54:53.000761 32304 solver.cpp:245]     Train net output #45: total_confidence = 3.10917e-07
I0407 12:54:53.000777 32304 sgd_solver.cpp:106] Iteration 13500, lr = 0.00973
I0407 12:56:05.249766 32304 solver.cpp:229] Iteration 14000, loss = 1.12447
I0407 12:56:05.249884 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 12:56:05.249905 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 12:56:05.249917 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 12:56:05.249929 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0
I0407 12:56:05.249941 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 12:56:05.249953 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.28125
I0407 12:56:05.249965 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 12:56:05.249976 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 12:56:05.249989 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 12:56:05.250000 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:56:05.250011 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:56:05.250023 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:56:05.250035 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:56:05.250046 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:56:05.250057 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:56:05.250069 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:56:05.250085 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:56:05.250097 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:56:05.250108 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:56:05.250119 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:56:05.250130 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:56:05.250143 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:56:05.250159 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.55637 (* 0.0454545 = 0.161653 loss)
I0407 12:56:05.250172 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.74476 (* 0.0454545 = 0.170216 loss)
I0407 12:56:05.250186 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.8887 (* 0.0454545 = 0.176759 loss)
I0407 12:56:05.250200 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.97477 (* 0.0454545 = 0.180671 loss)
I0407 12:56:05.250214 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.83111 (* 0.0454545 = 0.174142 loss)
I0407 12:56:05.250227 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.30182 (* 0.0454545 = 0.150083 loss)
I0407 12:56:05.250241 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.36846 (* 0.0454545 = 0.0622029 loss)
I0407 12:56:05.250255 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.685604 (* 0.0454545 = 0.0311638 loss)
I0407 12:56:05.250269 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0185948 (* 0.0454545 = 0.000845219 loss)
I0407 12:56:05.250283 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00622797 (* 0.0454545 = 0.000283089 loss)
I0407 12:56:05.250298 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.79846e-05 (* 0.0454545 = 2.18112e-06 loss)
I0407 12:56:05.250313 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.71909e-05 (* 0.0454545 = 2.14504e-06 loss)
I0407 12:56:05.250326 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.82902e-05 (* 0.0454545 = 2.19501e-06 loss)
I0407 12:56:05.250339 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.69562e-05 (* 0.0454545 = 2.13437e-06 loss)
I0407 12:56:05.250357 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.73028e-05 (* 0.0454545 = 2.15013e-06 loss)
I0407 12:56:05.250373 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.69488e-05 (* 0.0454545 = 2.13404e-06 loss)
I0407 12:56:05.250387 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.63936e-05 (* 0.0454545 = 2.1088e-06 loss)
I0407 12:56:05.250432 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.61924e-05 (* 0.0454545 = 2.09965e-06 loss)
I0407 12:56:05.250459 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.69265e-05 (* 0.0454545 = 2.13302e-06 loss)
I0407 12:56:05.250476 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.68556e-05 (* 0.0454545 = 2.1298e-06 loss)
I0407 12:56:05.250490 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.58197e-05 (* 0.0454545 = 2.08271e-06 loss)
I0407 12:56:05.250504 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.66955e-05 (* 0.0454545 = 2.12252e-06 loss)
I0407 12:56:05.250516 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:56:05.250527 32304 solver.cpp:245]     Train net output #45: total_confidence = 7.49931e-07
I0407 12:56:05.250542 32304 sgd_solver.cpp:106] Iteration 14000, lr = 0.00972
I0407 12:57:18.307550 32304 solver.cpp:229] Iteration 14500, loss = 1.12587
I0407 12:57:18.307687 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:57:18.307706 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 12:57:18.307719 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:57:18.307732 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 12:57:18.307744 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.3125
I0407 12:57:18.307756 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5625
I0407 12:57:18.307768 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.875
I0407 12:57:18.307780 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 12:57:18.307791 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:57:18.307803 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 12:57:18.307816 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:57:18.307827 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:57:18.307838 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:57:18.307850 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:57:18.307862 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:57:18.307873 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:57:18.307885 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:57:18.307898 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:57:18.307909 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:57:18.307922 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:57:18.307935 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:57:18.307946 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:57:18.307963 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.60099 (* 0.0454545 = 0.163681 loss)
I0407 12:57:18.307977 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 4.07002 (* 0.0454545 = 0.185001 loss)
I0407 12:57:18.307991 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.92394 (* 0.0454545 = 0.178361 loss)
I0407 12:57:18.308007 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.83378 (* 0.0454545 = 0.174263 loss)
I0407 12:57:18.308019 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.22042 (* 0.0454545 = 0.146383 loss)
I0407 12:57:18.308033 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.28258 (* 0.0454545 = 0.103754 loss)
I0407 12:57:18.308048 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.11224 (* 0.0454545 = 0.0505564 loss)
I0407 12:57:18.308061 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.19507 (* 0.0454545 = 0.00886683 loss)
I0407 12:57:18.308084 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.244889 (* 0.0454545 = 0.0111313 loss)
I0407 12:57:18.308099 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.251546 (* 0.0454545 = 0.0114339 loss)
I0407 12:57:18.308112 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.57996e-05 (* 0.0454545 = 2.0818e-06 loss)
I0407 12:57:18.308126 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.38607e-05 (* 0.0454545 = 1.99367e-06 loss)
I0407 12:57:18.308140 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.54047e-05 (* 0.0454545 = 2.06385e-06 loss)
I0407 12:57:18.308154 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.43738e-05 (* 0.0454545 = 2.01699e-06 loss)
I0407 12:57:18.308171 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.4053e-05 (* 0.0454545 = 2.00241e-06 loss)
I0407 12:57:18.308184 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.55651e-05 (* 0.0454545 = 2.07114e-06 loss)
I0407 12:57:18.308198 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.42397e-05 (* 0.0454545 = 2.0109e-06 loss)
I0407 12:57:18.308240 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.37438e-05 (* 0.0454545 = 1.98835e-06 loss)
I0407 12:57:18.308255 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.5746e-05 (* 0.0454545 = 2.07937e-06 loss)
I0407 12:57:18.308269 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.57215e-05 (* 0.0454545 = 2.07825e-06 loss)
I0407 12:57:18.308284 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.45118e-05 (* 0.0454545 = 2.02326e-06 loss)
I0407 12:57:18.308296 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.44108e-05 (* 0.0454545 = 2.01867e-06 loss)
I0407 12:57:18.308308 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:57:18.308320 32304 solver.cpp:245]     Train net output #45: total_confidence = 9.60734e-07
I0407 12:57:18.308336 32304 sgd_solver.cpp:106] Iteration 14500, lr = 0.00971
I0407 12:58:30.513821 32304 solver.cpp:338] Iteration 15000, Testing net (#0)
I0407 12:58:38.626343 32304 solver.cpp:393] Test loss: 0.98699
I0407 12:58:38.626404 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.266
I0407 12:58:38.626428 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.101
I0407 12:58:38.626449 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.074
I0407 12:58:38.626471 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.045
I0407 12:58:38.626492 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.212
I0407 12:58:38.626512 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.501
I0407 12:58:38.626531 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.894
I0407 12:58:38.626551 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 12:58:38.626572 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 12:58:38.626593 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 12:58:38.626612 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 12:58:38.626632 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 12:58:38.626652 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 12:58:38.626673 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 12:58:38.626696 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 12:58:38.626716 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 12:58:38.626736 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 12:58:38.626757 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 12:58:38.626777 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 12:58:38.626798 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 12:58:38.626818 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 12:58:38.626837 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 12:58:38.626864 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 3.26276 (* 0.0454545 = 0.148307 loss)
I0407 12:58:38.626889 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.6041 (* 0.0454545 = 0.163823 loss)
I0407 12:58:38.626915 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 3.70176 (* 0.0454545 = 0.168262 loss)
I0407 12:58:38.626945 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 3.75403 (* 0.0454545 = 0.170638 loss)
I0407 12:58:38.626970 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 3.66268 (* 0.0454545 = 0.166485 loss)
I0407 12:58:38.626994 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 2.45865 (* 0.0454545 = 0.111757 loss)
I0407 12:58:38.627017 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.911953 (* 0.0454545 = 0.0414524 loss)
I0407 12:58:38.627041 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.276891 (* 0.0454545 = 0.012586 loss)
I0407 12:58:38.627068 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0549173 (* 0.0454545 = 0.00249624 loss)
I0407 12:58:38.627092 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0254111 (* 0.0454545 = 0.00115505 loss)
I0407 12:58:38.627118 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 5.39416e-05 (* 0.0454545 = 2.45189e-06 loss)
I0407 12:58:38.627143 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 5.40329e-05 (* 0.0454545 = 2.45604e-06 loss)
I0407 12:58:38.627168 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 5.46662e-05 (* 0.0454545 = 2.48483e-06 loss)
I0407 12:58:38.627193 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 5.41745e-05 (* 0.0454545 = 2.46248e-06 loss)
I0407 12:58:38.627219 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 5.31443e-05 (* 0.0454545 = 2.41565e-06 loss)
I0407 12:58:38.627244 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 5.30951e-05 (* 0.0454545 = 2.41342e-06 loss)
I0407 12:58:38.627274 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 5.24283e-05 (* 0.0454545 = 2.38311e-06 loss)
I0407 12:58:38.627360 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 5.27454e-05 (* 0.0454545 = 2.39752e-06 loss)
I0407 12:58:38.627389 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 5.27395e-05 (* 0.0454545 = 2.39725e-06 loss)
I0407 12:58:38.627415 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 5.35108e-05 (* 0.0454545 = 2.43231e-06 loss)
I0407 12:58:38.627440 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 5.23556e-05 (* 0.0454545 = 2.3798e-06 loss)
I0407 12:58:38.627467 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 5.32623e-05 (* 0.0454545 = 2.42101e-06 loss)
I0407 12:58:38.627488 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0
I0407 12:58:38.627512 32304 solver.cpp:406]     Test net output #45: total_confidence = 1.04936e-06
I0407 12:58:38.661734 32304 solver.cpp:229] Iteration 15000, loss = 1.12355
I0407 12:58:38.661794 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 12:58:38.661821 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 12:58:38.661842 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 12:58:38.661864 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 12:58:38.661885 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.375
I0407 12:58:38.661906 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5625
I0407 12:58:38.661928 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 12:58:38.661952 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 12:58:38.661972 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 12:58:38.661994 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:58:38.662015 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:58:38.662036 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:58:38.662060 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:58:38.662087 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:58:38.662109 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:58:38.662132 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:58:38.662153 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:58:38.662173 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:58:38.662194 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:58:38.662214 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:58:38.662235 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:58:38.662257 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:58:38.662288 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.57599 (* 0.0454545 = 0.162545 loss)
I0407 12:58:38.662317 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.80582 (* 0.0454545 = 0.172992 loss)
I0407 12:58:38.662341 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.81019 (* 0.0454545 = 0.173191 loss)
I0407 12:58:38.662367 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.73905 (* 0.0454545 = 0.169957 loss)
I0407 12:58:38.662392 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.20142 (* 0.0454545 = 0.145519 loss)
I0407 12:58:38.662417 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.31695 (* 0.0454545 = 0.105316 loss)
I0407 12:58:38.662442 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.33144 (* 0.0454545 = 0.0605201 loss)
I0407 12:58:38.662467 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.690827 (* 0.0454545 = 0.0314012 loss)
I0407 12:58:38.662492 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.531585 (* 0.0454545 = 0.024163 loss)
I0407 12:58:38.662518 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0128052 (* 0.0454545 = 0.000582054 loss)
I0407 12:58:38.662575 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.00021113 (* 0.0454545 = 9.59684e-06 loss)
I0407 12:58:38.662601 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000215069 (* 0.0454545 = 9.77588e-06 loss)
I0407 12:58:38.662627 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000219362 (* 0.0454545 = 9.971e-06 loss)
I0407 12:58:38.662653 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00021373 (* 0.0454545 = 9.71502e-06 loss)
I0407 12:58:38.662680 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000213396 (* 0.0454545 = 9.6998e-06 loss)
I0407 12:58:38.662706 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000213721 (* 0.0454545 = 9.71461e-06 loss)
I0407 12:58:38.662736 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000209375 (* 0.0454545 = 9.51703e-06 loss)
I0407 12:58:38.662765 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000211698 (* 0.0454545 = 9.62262e-06 loss)
I0407 12:58:38.662792 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000210923 (* 0.0454545 = 9.58742e-06 loss)
I0407 12:58:38.662819 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000208066 (* 0.0454545 = 9.45755e-06 loss)
I0407 12:58:38.662845 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000204302 (* 0.0454545 = 9.28647e-06 loss)
I0407 12:58:38.662873 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000209248 (* 0.0454545 = 9.51128e-06 loss)
I0407 12:58:38.662894 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:58:38.662915 32304 solver.cpp:245]     Train net output #45: total_confidence = 1.07446e-06
I0407 12:58:38.662940 32304 sgd_solver.cpp:106] Iteration 15000, lr = 0.0097
I0407 12:59:50.798575 32304 solver.cpp:229] Iteration 15500, loss = 1.11427
I0407 12:59:50.798735 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 12:59:50.798758 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 12:59:50.798770 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 12:59:50.798782 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 12:59:50.798794 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 12:59:50.798806 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.25
I0407 12:59:50.798818 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 12:59:50.798830 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 12:59:50.798842 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 12:59:50.798853 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 12:59:50.798866 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 12:59:50.798877 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 12:59:50.798888 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 12:59:50.798899 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 12:59:50.798912 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 12:59:50.798925 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 12:59:50.798938 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 12:59:50.798949 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 12:59:50.798959 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 12:59:50.798971 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 12:59:50.798986 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 12:59:50.799010 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 12:59:50.799033 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.51083 (* 0.0454545 = 0.159583 loss)
I0407 12:59:50.799049 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.59325 (* 0.0454545 = 0.16333 loss)
I0407 12:59:50.799063 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.68259 (* 0.0454545 = 0.167391 loss)
I0407 12:59:50.799077 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.58703 (* 0.0454545 = 0.163047 loss)
I0407 12:59:50.799090 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.59534 (* 0.0454545 = 0.163424 loss)
I0407 12:59:50.799103 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.26616 (* 0.0454545 = 0.148462 loss)
I0407 12:59:50.799118 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.02044 (* 0.0454545 = 0.0918383 loss)
I0407 12:59:50.799130 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.357361 (* 0.0454545 = 0.0162437 loss)
I0407 12:59:50.799145 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.187516 (* 0.0454545 = 0.00852346 loss)
I0407 12:59:50.799160 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0102851 (* 0.0454545 = 0.000467506 loss)
I0407 12:59:50.799173 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.75849e-05 (* 0.0454545 = 2.16295e-06 loss)
I0407 12:59:50.799186 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.88441e-05 (* 0.0454545 = 2.22019e-06 loss)
I0407 12:59:50.799204 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.02118e-05 (* 0.0454545 = 2.28235e-06 loss)
I0407 12:59:50.799219 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.68582e-05 (* 0.0454545 = 2.12992e-06 loss)
I0407 12:59:50.799233 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.78269e-05 (* 0.0454545 = 2.17395e-06 loss)
I0407 12:59:50.799247 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.81885e-05 (* 0.0454545 = 2.19039e-06 loss)
I0407 12:59:50.799260 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.6389e-05 (* 0.0454545 = 2.10859e-06 loss)
I0407 12:59:50.799288 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.79426e-05 (* 0.0454545 = 2.17921e-06 loss)
I0407 12:59:50.799304 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.54352e-05 (* 0.0454545 = 2.06523e-06 loss)
I0407 12:59:50.799340 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.60013e-05 (* 0.0454545 = 2.09097e-06 loss)
I0407 12:59:50.799358 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.53196e-05 (* 0.0454545 = 2.05998e-06 loss)
I0407 12:59:50.799372 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.85276e-05 (* 0.0454545 = 2.2058e-06 loss)
I0407 12:59:50.799384 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 12:59:50.799396 32304 solver.cpp:245]     Train net output #45: total_confidence = 5.86139e-07
I0407 12:59:50.799412 32304 sgd_solver.cpp:106] Iteration 15500, lr = 0.00969
I0407 13:01:03.729580 32304 solver.cpp:229] Iteration 16000, loss = 1.09447
I0407 13:01:03.729776 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:01:03.729807 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 13:01:03.729828 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:01:03.729849 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 13:01:03.729871 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.40625
I0407 13:01:03.729892 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.53125
I0407 13:01:03.729913 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 13:01:03.729939 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 13:01:03.729961 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:01:03.729982 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:01:03.730003 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:01:03.730026 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:01:03.730046 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:01:03.730069 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:01:03.730093 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:01:03.730114 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:01:03.730136 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:01:03.730157 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:01:03.730178 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:01:03.730198 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:01:03.730221 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:01:03.730240 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:01:03.730268 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.63995 (* 0.0454545 = 0.165452 loss)
I0407 13:01:03.730295 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.85499 (* 0.0454545 = 0.175227 loss)
I0407 13:01:03.730320 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.86772 (* 0.0454545 = 0.175805 loss)
I0407 13:01:03.730347 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.96145 (* 0.0454545 = 0.180066 loss)
I0407 13:01:03.730373 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.24708 (* 0.0454545 = 0.147595 loss)
I0407 13:01:03.730398 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.64341 (* 0.0454545 = 0.120155 loss)
I0407 13:01:03.730423 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.79255 (* 0.0454545 = 0.0814794 loss)
I0407 13:01:03.730449 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.270611 (* 0.0454545 = 0.0123005 loss)
I0407 13:01:03.730476 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0247869 (* 0.0454545 = 0.00112668 loss)
I0407 13:01:03.730502 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0082991 (* 0.0454545 = 0.000377232 loss)
I0407 13:01:03.730530 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.73585e-05 (* 0.0454545 = 2.15266e-06 loss)
I0407 13:01:03.730556 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 5.00976e-05 (* 0.0454545 = 2.27717e-06 loss)
I0407 13:01:03.730581 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.73227e-05 (* 0.0454545 = 2.15103e-06 loss)
I0407 13:01:03.730607 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.56735e-05 (* 0.0454545 = 2.07607e-06 loss)
I0407 13:01:03.730634 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 5.04389e-05 (* 0.0454545 = 2.29268e-06 loss)
I0407 13:01:03.730661 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.64614e-05 (* 0.0454545 = 2.11188e-06 loss)
I0407 13:01:03.730687 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.43579e-05 (* 0.0454545 = 2.01627e-06 loss)
I0407 13:01:03.730738 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.65176e-05 (* 0.0454545 = 2.11444e-06 loss)
I0407 13:01:03.730769 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.38083e-05 (* 0.0454545 = 1.99128e-06 loss)
I0407 13:01:03.730798 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.68643e-05 (* 0.0454545 = 2.13019e-06 loss)
I0407 13:01:03.730824 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.51613e-05 (* 0.0454545 = 2.05279e-06 loss)
I0407 13:01:03.730852 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.47472e-05 (* 0.0454545 = 2.03396e-06 loss)
I0407 13:01:03.730877 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:01:03.730903 32304 solver.cpp:245]     Train net output #45: total_confidence = 1.51786e-05
I0407 13:01:03.730928 32304 sgd_solver.cpp:106] Iteration 16000, lr = 0.00968
I0407 13:02:15.661108 32304 solver.cpp:229] Iteration 16500, loss = 1.08709
I0407 13:02:15.661239 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:02:15.661259 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 13:02:15.661273 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:02:15.661284 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 13:02:15.661296 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 13:02:15.661309 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 13:02:15.661320 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.5625
I0407 13:02:15.661331 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 13:02:15.661344 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:02:15.661355 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 13:02:15.661366 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:02:15.661377 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:02:15.661389 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:02:15.661401 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:02:15.661412 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:02:15.661423 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:02:15.661435 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:02:15.661447 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:02:15.661458 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:02:15.661468 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:02:15.661480 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:02:15.661491 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:02:15.661507 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.39154 (* 0.0454545 = 0.154161 loss)
I0407 13:02:15.661522 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.86248 (* 0.0454545 = 0.175567 loss)
I0407 13:02:15.661536 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.67656 (* 0.0454545 = 0.167116 loss)
I0407 13:02:15.661550 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.57576 (* 0.0454545 = 0.162535 loss)
I0407 13:02:15.661563 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.76299 (* 0.0454545 = 0.171045 loss)
I0407 13:02:15.661577 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.84917 (* 0.0454545 = 0.129508 loss)
I0407 13:02:15.661591 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.13804 (* 0.0454545 = 0.0971838 loss)
I0407 13:02:15.661604 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.648027 (* 0.0454545 = 0.0294558 loss)
I0407 13:02:15.661618 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.343457 (* 0.0454545 = 0.0156117 loss)
I0407 13:02:15.661633 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.402069 (* 0.0454545 = 0.0182759 loss)
I0407 13:02:15.661646 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000230636 (* 0.0454545 = 1.04835e-05 loss)
I0407 13:02:15.661660 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000234747 (* 0.0454545 = 1.06703e-05 loss)
I0407 13:02:15.661674 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000227985 (* 0.0454545 = 1.03629e-05 loss)
I0407 13:02:15.661692 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000215813 (* 0.0454545 = 9.8097e-06 loss)
I0407 13:02:15.661706 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000235417 (* 0.0454545 = 1.07008e-05 loss)
I0407 13:02:15.661720 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000226342 (* 0.0454545 = 1.02883e-05 loss)
I0407 13:02:15.661734 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000204814 (* 0.0454545 = 9.30971e-06 loss)
I0407 13:02:15.661767 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000216996 (* 0.0454545 = 9.86347e-06 loss)
I0407 13:02:15.661782 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000196647 (* 0.0454545 = 8.9385e-06 loss)
I0407 13:02:15.661795 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000239289 (* 0.0454545 = 1.08768e-05 loss)
I0407 13:02:15.661809 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000226839 (* 0.0454545 = 1.03108e-05 loss)
I0407 13:02:15.661823 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000216447 (* 0.0454545 = 9.83848e-06 loss)
I0407 13:02:15.661834 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:02:15.661846 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.10976e-05
I0407 13:02:15.661861 32304 sgd_solver.cpp:106] Iteration 16500, lr = 0.00967
I0407 13:03:27.846272 32304 solver.cpp:229] Iteration 17000, loss = 1.08178
I0407 13:03:27.846431 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0
I0407 13:03:27.846459 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 13:03:27.846482 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 13:03:27.846501 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.21875
I0407 13:03:27.846524 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.34375
I0407 13:03:27.846544 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 13:03:27.846565 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 13:03:27.846587 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:03:27.846608 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:03:27.846631 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 13:03:27.846652 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:03:27.846671 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:03:27.846693 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:03:27.846716 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:03:27.846740 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:03:27.846760 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:03:27.846782 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:03:27.846803 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:03:27.846823 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:03:27.846844 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:03:27.846866 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:03:27.846887 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:03:27.846915 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.88297 (* 0.0454545 = 0.176499 loss)
I0407 13:03:27.846942 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.7447 (* 0.0454545 = 0.170214 loss)
I0407 13:03:27.846967 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.75763 (* 0.0454545 = 0.170801 loss)
I0407 13:03:27.846993 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.69891 (* 0.0454545 = 0.168132 loss)
I0407 13:03:27.847036 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.1404 (* 0.0454545 = 0.142745 loss)
I0407 13:03:27.847064 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.63241 (* 0.0454545 = 0.119655 loss)
I0407 13:03:27.847095 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.15032 (* 0.0454545 = 0.0522874 loss)
I0407 13:03:27.847122 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.482208 (* 0.0454545 = 0.0219185 loss)
I0407 13:03:27.847148 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.323908 (* 0.0454545 = 0.0147231 loss)
I0407 13:03:27.847174 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.375251 (* 0.0454545 = 0.0170569 loss)
I0407 13:03:27.847200 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000120932 (* 0.0454545 = 5.49691e-06 loss)
I0407 13:03:27.847228 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000118039 (* 0.0454545 = 5.36541e-06 loss)
I0407 13:03:27.847254 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000118112 (* 0.0454545 = 5.36872e-06 loss)
I0407 13:03:27.847278 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000111331 (* 0.0454545 = 5.06048e-06 loss)
I0407 13:03:27.847304 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000114975 (* 0.0454545 = 5.22613e-06 loss)
I0407 13:03:27.847353 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000114669 (* 0.0454545 = 5.21224e-06 loss)
I0407 13:03:27.847383 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000104382 (* 0.0454545 = 4.74465e-06 loss)
I0407 13:03:27.847429 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000116089 (* 0.0454545 = 5.27679e-06 loss)
I0407 13:03:27.847457 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000100941 (* 0.0454545 = 4.58822e-06 loss)
I0407 13:03:27.847486 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000123713 (* 0.0454545 = 5.62334e-06 loss)
I0407 13:03:27.847515 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000116706 (* 0.0454545 = 5.30483e-06 loss)
I0407 13:03:27.847544 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000113506 (* 0.0454545 = 5.15938e-06 loss)
I0407 13:03:27.847566 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:03:27.847587 32304 solver.cpp:245]     Train net output #45: total_confidence = 1.00219e-05
I0407 13:03:27.847612 32304 sgd_solver.cpp:106] Iteration 17000, lr = 0.00966
I0407 13:04:39.758139 32304 solver.cpp:229] Iteration 17500, loss = 1.07743
I0407 13:04:39.758301 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:04:39.758329 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 13:04:39.758352 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 13:04:39.758381 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 13:04:39.758404 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.0625
I0407 13:04:39.758425 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.28125
I0407 13:04:39.758445 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 13:04:39.758476 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:04:39.758496 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:04:39.758518 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:04:39.758539 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:04:39.758560 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:04:39.758581 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:04:39.758605 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:04:39.758627 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:04:39.758648 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:04:39.758668 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:04:39.758690 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:04:39.758718 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:04:39.758738 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:04:39.758759 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:04:39.758788 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:04:39.758816 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.51511 (* 0.0454545 = 0.159778 loss)
I0407 13:04:39.758843 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.71469 (* 0.0454545 = 0.168849 loss)
I0407 13:04:39.758867 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.80191 (* 0.0454545 = 0.172814 loss)
I0407 13:04:39.758900 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.63728 (* 0.0454545 = 0.165331 loss)
I0407 13:04:39.758929 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.76095 (* 0.0454545 = 0.170952 loss)
I0407 13:04:39.758965 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.21217 (* 0.0454545 = 0.146008 loss)
I0407 13:04:39.758992 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.12876 (* 0.0454545 = 0.0513073 loss)
I0407 13:04:39.759017 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.361488 (* 0.0454545 = 0.0164313 loss)
I0407 13:04:39.759044 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.183225 (* 0.0454545 = 0.00832842 loss)
I0407 13:04:39.759070 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00448206 (* 0.0454545 = 0.00020373 loss)
I0407 13:04:39.759096 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 2.97729e-05 (* 0.0454545 = 1.35332e-06 loss)
I0407 13:04:39.759124 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 2.92047e-05 (* 0.0454545 = 1.32749e-06 loss)
I0407 13:04:39.759150 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 2.88358e-05 (* 0.0454545 = 1.31072e-06 loss)
I0407 13:04:39.759183 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 2.75932e-05 (* 0.0454545 = 1.25424e-06 loss)
I0407 13:04:39.759210 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 2.87315e-05 (* 0.0454545 = 1.30598e-06 loss)
I0407 13:04:39.759237 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 2.9391e-05 (* 0.0454545 = 1.33596e-06 loss)
I0407 13:04:39.759269 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 2.42475e-05 (* 0.0454545 = 1.10216e-06 loss)
I0407 13:04:39.759348 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 2.81074e-05 (* 0.0454545 = 1.27761e-06 loss)
I0407 13:04:39.759379 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 2.45344e-05 (* 0.0454545 = 1.1152e-06 loss)
I0407 13:04:39.759407 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 3.05423e-05 (* 0.0454545 = 1.38829e-06 loss)
I0407 13:04:39.759436 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 2.76641e-05 (* 0.0454545 = 1.25746e-06 loss)
I0407 13:04:39.759467 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 2.77125e-05 (* 0.0454545 = 1.25966e-06 loss)
I0407 13:04:39.759491 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:04:39.759513 32304 solver.cpp:245]     Train net output #45: total_confidence = 5.39093e-05
I0407 13:04:39.759542 32304 sgd_solver.cpp:106] Iteration 17500, lr = 0.00965
I0407 13:05:52.230103 32304 solver.cpp:229] Iteration 18000, loss = 1.07548
I0407 13:05:52.230270 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 13:05:52.230295 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:05:52.230321 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 13:05:52.230336 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 13:05:52.230350 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.125
I0407 13:05:52.230361 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.25
I0407 13:05:52.230373 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:05:52.230384 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:05:52.230396 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:05:52.230408 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:05:52.230420 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:05:52.230432 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:05:52.230443 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:05:52.230454 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:05:52.230474 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:05:52.230497 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:05:52.230516 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:05:52.230536 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:05:52.230552 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:05:52.230564 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:05:52.230576 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:05:52.230587 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:05:52.230603 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.94299 (* 0.0454545 = 0.179227 loss)
I0407 13:05:52.230618 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.94119 (* 0.0454545 = 0.179145 loss)
I0407 13:05:52.230631 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.8703 (* 0.0454545 = 0.175923 loss)
I0407 13:05:52.230645 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.93693 (* 0.0454545 = 0.178951 loss)
I0407 13:05:52.230659 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.77511 (* 0.0454545 = 0.171596 loss)
I0407 13:05:52.230672 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.54211 (* 0.0454545 = 0.161005 loss)
I0407 13:05:52.230685 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.54734 (* 0.0454545 = 0.0703338 loss)
I0407 13:05:52.230700 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.525245 (* 0.0454545 = 0.0238748 loss)
I0407 13:05:52.230713 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.243535 (* 0.0454545 = 0.0110698 loss)
I0407 13:05:52.230728 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0181648 (* 0.0454545 = 0.00082567 loss)
I0407 13:05:52.230742 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000460941 (* 0.0454545 = 2.09519e-05 loss)
I0407 13:05:52.230756 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000440406 (* 0.0454545 = 2.00185e-05 loss)
I0407 13:05:52.230770 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000455956 (* 0.0454545 = 2.07253e-05 loss)
I0407 13:05:52.230784 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000437028 (* 0.0454545 = 1.98649e-05 loss)
I0407 13:05:52.230798 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000442168 (* 0.0454545 = 2.00985e-05 loss)
I0407 13:05:52.230813 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000450935 (* 0.0454545 = 2.04971e-05 loss)
I0407 13:05:52.230826 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.00042334 (* 0.0454545 = 1.92427e-05 loss)
I0407 13:05:52.230859 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000445286 (* 0.0454545 = 2.02403e-05 loss)
I0407 13:05:52.230873 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000405578 (* 0.0454545 = 1.84354e-05 loss)
I0407 13:05:52.230887 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000463266 (* 0.0454545 = 2.10575e-05 loss)
I0407 13:05:52.230901 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000443044 (* 0.0454545 = 2.01384e-05 loss)
I0407 13:05:52.230914 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000437491 (* 0.0454545 = 1.98859e-05 loss)
I0407 13:05:52.230931 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:05:52.230942 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.97196e-06
I0407 13:05:52.230957 32304 sgd_solver.cpp:106] Iteration 18000, lr = 0.00964
I0407 13:07:05.062777 32304 solver.cpp:229] Iteration 18500, loss = 1.06481
I0407 13:07:05.062933 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:07:05.062953 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 13:07:05.062966 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 13:07:05.062978 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 13:07:05.062991 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.125
I0407 13:07:05.063004 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 13:07:05.063015 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 13:07:05.063027 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:07:05.063040 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:07:05.063051 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:07:05.063063 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:07:05.063074 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:07:05.063086 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:07:05.063097 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:07:05.063108 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:07:05.063119 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:07:05.063132 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:07:05.063143 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:07:05.063154 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:07:05.063166 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:07:05.063179 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:07:05.063189 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:07:05.063205 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.70428 (* 0.0454545 = 0.168376 loss)
I0407 13:07:05.063220 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.75331 (* 0.0454545 = 0.170605 loss)
I0407 13:07:05.063235 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.89899 (* 0.0454545 = 0.177227 loss)
I0407 13:07:05.063248 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.50627 (* 0.0454545 = 0.159376 loss)
I0407 13:07:05.063261 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.57656 (* 0.0454545 = 0.162571 loss)
I0407 13:07:05.063276 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.25075 (* 0.0454545 = 0.147761 loss)
I0407 13:07:05.063289 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.54956 (* 0.0454545 = 0.0704346 loss)
I0407 13:07:05.063303 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.458681 (* 0.0454545 = 0.0208491 loss)
I0407 13:07:05.063335 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.195106 (* 0.0454545 = 0.00886845 loss)
I0407 13:07:05.063354 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0119287 (* 0.0454545 = 0.000542214 loss)
I0407 13:07:05.063367 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.26711e-05 (* 0.0454545 = 3.75778e-06 loss)
I0407 13:07:05.063382 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 8.15325e-05 (* 0.0454545 = 3.70602e-06 loss)
I0407 13:07:05.063396 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 8.43214e-05 (* 0.0454545 = 3.83279e-06 loss)
I0407 13:07:05.063410 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.06887e-05 (* 0.0454545 = 3.66767e-06 loss)
I0407 13:07:05.063424 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 8.08396e-05 (* 0.0454545 = 3.67453e-06 loss)
I0407 13:07:05.063438 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 8.05399e-05 (* 0.0454545 = 3.66091e-06 loss)
I0407 13:07:05.063452 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 7.48016e-05 (* 0.0454545 = 3.40008e-06 loss)
I0407 13:07:05.063484 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 8.10833e-05 (* 0.0454545 = 3.6856e-06 loss)
I0407 13:07:05.063500 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.48264e-05 (* 0.0454545 = 3.4012e-06 loss)
I0407 13:07:05.063514 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 8.7166e-05 (* 0.0454545 = 3.96209e-06 loss)
I0407 13:07:05.063529 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 8.21589e-05 (* 0.0454545 = 3.73449e-06 loss)
I0407 13:07:05.063542 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 8.08841e-05 (* 0.0454545 = 3.67655e-06 loss)
I0407 13:07:05.063555 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:07:05.063565 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000139739
I0407 13:07:05.063580 32304 sgd_solver.cpp:106] Iteration 18500, lr = 0.00963
I0407 13:08:17.650177 32304 solver.cpp:229] Iteration 19000, loss = 1.06155
I0407 13:08:17.650333 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:08:17.650353 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:08:17.650367 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.1875
I0407 13:08:17.650379 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 13:08:17.650391 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.125
I0407 13:08:17.650403 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 13:08:17.650415 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 13:08:17.650427 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:08:17.650439 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:08:17.650451 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 13:08:17.650463 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:08:17.650475 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:08:17.650492 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:08:17.650513 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:08:17.650537 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:08:17.650562 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:08:17.650586 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:08:17.650612 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:08:17.650635 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:08:17.650658 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:08:17.650684 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:08:17.650710 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:08:17.650743 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.48602 (* 0.0454545 = 0.158456 loss)
I0407 13:08:17.650769 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.63165 (* 0.0454545 = 0.165075 loss)
I0407 13:08:17.650794 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.72839 (* 0.0454545 = 0.169472 loss)
I0407 13:08:17.650820 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.67044 (* 0.0454545 = 0.166838 loss)
I0407 13:08:17.650848 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.64869 (* 0.0454545 = 0.16585 loss)
I0407 13:08:17.650881 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.13381 (* 0.0454545 = 0.142446 loss)
I0407 13:08:17.650910 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.78952 (* 0.0454545 = 0.0813416 loss)
I0407 13:08:17.650944 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.407068 (* 0.0454545 = 0.0185031 loss)
I0407 13:08:17.650977 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.389785 (* 0.0454545 = 0.0177175 loss)
I0407 13:08:17.651010 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.404496 (* 0.0454545 = 0.0183862 loss)
I0407 13:08:17.651042 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.00045426 (* 0.0454545 = 2.06482e-05 loss)
I0407 13:08:17.651077 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000424403 (* 0.0454545 = 1.92911e-05 loss)
I0407 13:08:17.651110 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000439689 (* 0.0454545 = 1.99859e-05 loss)
I0407 13:08:17.651139 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000420219 (* 0.0454545 = 1.91009e-05 loss)
I0407 13:08:17.651165 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000459429 (* 0.0454545 = 2.08831e-05 loss)
I0407 13:08:17.651190 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000445554 (* 0.0454545 = 2.02524e-05 loss)
I0407 13:08:17.651214 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000411047 (* 0.0454545 = 1.86839e-05 loss)
I0407 13:08:17.651259 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000432739 (* 0.0454545 = 1.967e-05 loss)
I0407 13:08:17.651291 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.00042319 (* 0.0454545 = 1.92359e-05 loss)
I0407 13:08:17.651342 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000464106 (* 0.0454545 = 2.10957e-05 loss)
I0407 13:08:17.651378 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000433624 (* 0.0454545 = 1.97102e-05 loss)
I0407 13:08:17.651410 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000419706 (* 0.0454545 = 1.90775e-05 loss)
I0407 13:08:17.651437 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:08:17.651464 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.137e-05
I0407 13:08:17.651495 32304 sgd_solver.cpp:106] Iteration 19000, lr = 0.00962
I0407 13:09:28.365523 32304 solver.cpp:229] Iteration 19500, loss = 1.05412
I0407 13:09:28.365666 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 13:09:28.365686 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:09:28.365700 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:09:28.365711 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:09:28.365723 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 13:09:28.365736 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 13:09:28.365746 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.84375
I0407 13:09:28.365758 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 13:09:28.365769 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:09:28.365782 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:09:28.365792 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:09:28.365804 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:09:28.365816 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:09:28.365828 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:09:28.365839 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:09:28.365851 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:09:28.365862 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:09:28.365874 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:09:28.365885 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:09:28.365896 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:09:28.365907 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:09:28.365921 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:09:28.365943 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.46512 (* 0.0454545 = 0.157505 loss)
I0407 13:09:28.365970 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.51448 (* 0.0454545 = 0.159749 loss)
I0407 13:09:28.365998 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.65916 (* 0.0454545 = 0.166325 loss)
I0407 13:09:28.366020 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.60531 (* 0.0454545 = 0.163878 loss)
I0407 13:09:28.366044 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.53149 (* 0.0454545 = 0.160522 loss)
I0407 13:09:28.366068 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.73712 (* 0.0454545 = 0.124415 loss)
I0407 13:09:28.366094 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.978403 (* 0.0454545 = 0.0444729 loss)
I0407 13:09:28.366111 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.194826 (* 0.0454545 = 0.00885573 loss)
I0407 13:09:28.366125 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.201541 (* 0.0454545 = 0.00916095 loss)
I0407 13:09:28.366139 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.219996 (* 0.0454545 = 0.00999983 loss)
I0407 13:09:28.366153 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000216648 (* 0.0454545 = 9.84765e-06 loss)
I0407 13:09:28.366168 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000195975 (* 0.0454545 = 8.90795e-06 loss)
I0407 13:09:28.366181 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000216876 (* 0.0454545 = 9.85798e-06 loss)
I0407 13:09:28.366195 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000204178 (* 0.0454545 = 9.28082e-06 loss)
I0407 13:09:28.366210 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000208135 (* 0.0454545 = 9.4607e-06 loss)
I0407 13:09:28.366225 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.00021079 (* 0.0454545 = 9.58138e-06 loss)
I0407 13:09:28.366238 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.00020476 (* 0.0454545 = 9.3073e-06 loss)
I0407 13:09:28.366271 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000212781 (* 0.0454545 = 9.67185e-06 loss)
I0407 13:09:28.366286 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000203256 (* 0.0454545 = 9.2389e-06 loss)
I0407 13:09:28.366299 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000224097 (* 0.0454545 = 1.01862e-05 loss)
I0407 13:09:28.366313 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000211914 (* 0.0454545 = 9.63245e-06 loss)
I0407 13:09:28.366328 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000201713 (* 0.0454545 = 9.16875e-06 loss)
I0407 13:09:28.366339 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:09:28.366351 32304 solver.cpp:245]     Train net output #45: total_confidence = 1.06164e-05
I0407 13:09:28.366367 32304 sgd_solver.cpp:106] Iteration 19500, lr = 0.00961
I0407 13:10:41.752387 32304 solver.cpp:338] Iteration 20000, Testing net (#0)
I0407 13:10:49.810289 32304 solver.cpp:393] Test loss: 0.940548
I0407 13:10:49.810351 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.066
I0407 13:10:49.810369 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.097
I0407 13:10:49.810381 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.072
I0407 13:10:49.810394 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.096
I0407 13:10:49.810405 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.213
I0407 13:10:49.810417 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.501
I0407 13:10:49.810430 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.894
I0407 13:10:49.810441 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 13:10:49.810451 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 13:10:49.810463 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 13:10:49.810474 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 13:10:49.810485 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 13:10:49.810497 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 13:10:49.810508 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 13:10:49.810519 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 13:10:49.810530 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 13:10:49.810542 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 13:10:49.810554 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 13:10:49.810564 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 13:10:49.810575 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 13:10:49.810587 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 13:10:49.810598 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 13:10:49.810614 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 3.47632 (* 0.0454545 = 0.158014 loss)
I0407 13:10:49.810628 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.45262 (* 0.0454545 = 0.156937 loss)
I0407 13:10:49.810642 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 3.41067 (* 0.0454545 = 0.15503 loss)
I0407 13:10:49.810655 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 3.39505 (* 0.0454545 = 0.15432 loss)
I0407 13:10:49.810669 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 3.40316 (* 0.0454545 = 0.154689 loss)
I0407 13:10:49.810683 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 2.40369 (* 0.0454545 = 0.109259 loss)
I0407 13:10:49.810696 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.823994 (* 0.0454545 = 0.0374543 loss)
I0407 13:10:49.810719 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.253687 (* 0.0454545 = 0.0115312 loss)
I0407 13:10:49.810734 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.04729 (* 0.0454545 = 0.00214954 loss)
I0407 13:10:49.810746 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0224433 (* 0.0454545 = 0.00102015 loss)
I0407 13:10:49.810760 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 0.000268761 (* 0.0454545 = 1.22164e-05 loss)
I0407 13:10:49.810775 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 0.000245358 (* 0.0454545 = 1.11526e-05 loss)
I0407 13:10:49.810788 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 0.000274862 (* 0.0454545 = 1.24937e-05 loss)
I0407 13:10:49.810806 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 0.000253517 (* 0.0454545 = 1.15235e-05 loss)
I0407 13:10:49.810820 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 0.000261973 (* 0.0454545 = 1.19078e-05 loss)
I0407 13:10:49.810834 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 0.000260054 (* 0.0454545 = 1.18206e-05 loss)
I0407 13:10:49.810847 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 0.000241896 (* 0.0454545 = 1.09953e-05 loss)
I0407 13:10:49.810900 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 0.000269478 (* 0.0454545 = 1.2249e-05 loss)
I0407 13:10:49.810915 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 0.000245819 (* 0.0454545 = 1.11736e-05 loss)
I0407 13:10:49.810933 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 0.000277784 (* 0.0454545 = 1.26266e-05 loss)
I0407 13:10:49.810947 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 0.000258288 (* 0.0454545 = 1.17404e-05 loss)
I0407 13:10:49.810961 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 0.000257337 (* 0.0454545 = 1.16971e-05 loss)
I0407 13:10:49.810972 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0.001
I0407 13:10:49.810984 32304 solver.cpp:406]     Test net output #45: total_confidence = 3.90288e-05
I0407 13:10:49.845671 32304 solver.cpp:229] Iteration 20000, loss = 1.04859
I0407 13:10:49.845736 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.15625
I0407 13:10:49.845754 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:10:49.845767 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:10:49.845779 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:10:49.845791 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 13:10:49.845803 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 13:10:49.845815 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.84375
I0407 13:10:49.845826 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 13:10:49.845839 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:10:49.845850 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:10:49.845862 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:10:49.845873 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:10:49.845885 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:10:49.845897 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:10:49.845909 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:10:49.845921 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:10:49.845932 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:10:49.845944 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:10:49.845955 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:10:49.845966 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:10:49.845978 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:10:49.845995 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:10:49.846035 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.30585 (* 0.0454545 = 0.150266 loss)
I0407 13:10:49.846063 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.69567 (* 0.0454545 = 0.167985 loss)
I0407 13:10:49.846096 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.53456 (* 0.0454545 = 0.160662 loss)
I0407 13:10:49.846117 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.83411 (* 0.0454545 = 0.174278 loss)
I0407 13:10:49.846130 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.42679 (* 0.0454545 = 0.155763 loss)
I0407 13:10:49.846144 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.48457 (* 0.0454545 = 0.112935 loss)
I0407 13:10:49.846158 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.860367 (* 0.0454545 = 0.0391076 loss)
I0407 13:10:49.846171 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.602182 (* 0.0454545 = 0.0273719 loss)
I0407 13:10:49.846185 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.172972 (* 0.0454545 = 0.00786237 loss)
I0407 13:10:49.846226 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00986224 (* 0.0454545 = 0.000448284 loss)
I0407 13:10:49.846242 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 5.3998e-05 (* 0.0454545 = 2.45446e-06 loss)
I0407 13:10:49.846256 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 5.07841e-05 (* 0.0454545 = 2.30837e-06 loss)
I0407 13:10:49.846271 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.78435e-05 (* 0.0454545 = 2.62925e-06 loss)
I0407 13:10:49.846283 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 5.31373e-05 (* 0.0454545 = 2.41533e-06 loss)
I0407 13:10:49.846297 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 5.44806e-05 (* 0.0454545 = 2.47639e-06 loss)
I0407 13:10:49.846312 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 5.25223e-05 (* 0.0454545 = 2.38738e-06 loss)
I0407 13:10:49.846325 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.92374e-05 (* 0.0454545 = 2.23806e-06 loss)
I0407 13:10:49.846339 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 5.35376e-05 (* 0.0454545 = 2.43353e-06 loss)
I0407 13:10:49.846354 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 5.27236e-05 (* 0.0454545 = 2.39653e-06 loss)
I0407 13:10:49.846366 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 5.69384e-05 (* 0.0454545 = 2.58811e-06 loss)
I0407 13:10:49.846380 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 5.25691e-05 (* 0.0454545 = 2.38951e-06 loss)
I0407 13:10:49.846395 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 5.34521e-05 (* 0.0454545 = 2.42964e-06 loss)
I0407 13:10:49.846412 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:10:49.846446 32304 solver.cpp:245]     Train net output #45: total_confidence = 5.20062e-05
I0407 13:10:49.846474 32304 sgd_solver.cpp:106] Iteration 20000, lr = 0.0096
I0407 13:12:01.369567 32304 solver.cpp:229] Iteration 20500, loss = 1.04538
I0407 13:12:01.369738 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:12:01.369767 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:12:01.369788 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 13:12:01.369810 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:12:01.369832 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 13:12:01.369853 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.25
I0407 13:12:01.369874 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 13:12:01.369895 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:12:01.369920 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:12:01.369943 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:12:01.369963 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:12:01.369984 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:12:01.370004 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:12:01.370026 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:12:01.370050 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:12:01.370071 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:12:01.370095 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:12:01.370115 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:12:01.370134 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:12:01.370156 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:12:01.370177 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:12:01.370196 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:12:01.370223 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.46333 (* 0.0454545 = 0.157424 loss)
I0407 13:12:01.370249 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.83943 (* 0.0454545 = 0.174519 loss)
I0407 13:12:01.370275 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.88669 (* 0.0454545 = 0.176668 loss)
I0407 13:12:01.370301 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.60904 (* 0.0454545 = 0.164047 loss)
I0407 13:12:01.370326 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.51237 (* 0.0454545 = 0.159653 loss)
I0407 13:12:01.370352 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.40391 (* 0.0454545 = 0.154723 loss)
I0407 13:12:01.370393 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.68306 (* 0.0454545 = 0.0765028 loss)
I0407 13:12:01.370422 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.494337 (* 0.0454545 = 0.0224699 loss)
I0407 13:12:01.370448 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.178321 (* 0.0454545 = 0.0081055 loss)
I0407 13:12:01.370474 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.200152 (* 0.0454545 = 0.00909783 loss)
I0407 13:12:01.370501 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000580706 (* 0.0454545 = 2.63957e-05 loss)
I0407 13:12:01.370527 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000535188 (* 0.0454545 = 2.43267e-05 loss)
I0407 13:12:01.370553 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000605625 (* 0.0454545 = 2.75284e-05 loss)
I0407 13:12:01.370579 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000526134 (* 0.0454545 = 2.39152e-05 loss)
I0407 13:12:01.370604 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000565845 (* 0.0454545 = 2.57202e-05 loss)
I0407 13:12:01.370630 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000561883 (* 0.0454545 = 2.55401e-05 loss)
I0407 13:12:01.370656 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000547644 (* 0.0454545 = 2.48929e-05 loss)
I0407 13:12:01.370702 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.00059504 (* 0.0454545 = 2.70473e-05 loss)
I0407 13:12:01.370730 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000551821 (* 0.0454545 = 2.50828e-05 loss)
I0407 13:12:01.370767 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.00059032 (* 0.0454545 = 2.68327e-05 loss)
I0407 13:12:01.370797 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000574286 (* 0.0454545 = 2.61039e-05 loss)
I0407 13:12:01.370827 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000544677 (* 0.0454545 = 2.47581e-05 loss)
I0407 13:12:01.370851 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:12:01.370872 32304 solver.cpp:245]     Train net output #45: total_confidence = 8.98801e-05
I0407 13:12:01.370896 32304 sgd_solver.cpp:106] Iteration 20500, lr = 0.00959
I0407 13:13:13.786046 32304 solver.cpp:229] Iteration 21000, loss = 1.03751
I0407 13:13:13.786197 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.15625
I0407 13:13:13.786217 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:13:13.786231 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 13:13:13.786243 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:13:13.786255 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 13:13:13.786267 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 13:13:13.786278 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 13:13:13.786289 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:13:13.786301 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:13:13.786314 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:13:13.786324 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:13:13.786336 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:13:13.786347 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:13:13.786360 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:13:13.786370 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:13:13.786382 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:13:13.786393 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:13:13.786412 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:13:13.786437 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:13:13.786451 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:13:13.786463 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:13:13.786478 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:13:13.786502 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.46957 (* 0.0454545 = 0.157708 loss)
I0407 13:13:13.786530 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.48705 (* 0.0454545 = 0.158502 loss)
I0407 13:13:13.786557 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.54422 (* 0.0454545 = 0.161101 loss)
I0407 13:13:13.786584 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.41736 (* 0.0454545 = 0.155334 loss)
I0407 13:13:13.786602 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.33659 (* 0.0454545 = 0.151663 loss)
I0407 13:13:13.786615 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.97824 (* 0.0454545 = 0.135374 loss)
I0407 13:13:13.786629 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.86642 (* 0.0454545 = 0.0848371 loss)
I0407 13:13:13.786643 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.712933 (* 0.0454545 = 0.0324061 loss)
I0407 13:13:13.786658 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.26014 (* 0.0454545 = 0.0118246 loss)
I0407 13:13:13.786672 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0287164 (* 0.0454545 = 0.00130529 loss)
I0407 13:13:13.786686 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000436527 (* 0.0454545 = 1.98421e-05 loss)
I0407 13:13:13.786700 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000405512 (* 0.0454545 = 1.84323e-05 loss)
I0407 13:13:13.786715 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000435371 (* 0.0454545 = 1.97896e-05 loss)
I0407 13:13:13.786727 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000387669 (* 0.0454545 = 1.76213e-05 loss)
I0407 13:13:13.786741 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000424267 (* 0.0454545 = 1.92849e-05 loss)
I0407 13:13:13.786756 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000416823 (* 0.0454545 = 1.89465e-05 loss)
I0407 13:13:13.786769 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000409382 (* 0.0454545 = 1.86083e-05 loss)
I0407 13:13:13.786801 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.00041957 (* 0.0454545 = 1.90714e-05 loss)
I0407 13:13:13.786816 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000388396 (* 0.0454545 = 1.76543e-05 loss)
I0407 13:13:13.786830 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000439211 (* 0.0454545 = 1.99641e-05 loss)
I0407 13:13:13.786844 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000423408 (* 0.0454545 = 1.92458e-05 loss)
I0407 13:13:13.786859 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000408142 (* 0.0454545 = 1.85519e-05 loss)
I0407 13:13:13.786870 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:13:13.786885 32304 solver.cpp:245]     Train net output #45: total_confidence = 4.92164e-05
I0407 13:13:13.786901 32304 sgd_solver.cpp:106] Iteration 21000, lr = 0.00958
I0407 13:14:25.239230 32304 solver.cpp:229] Iteration 21500, loss = 1.03822
I0407 13:14:25.239367 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:14:25.239399 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:14:25.239416 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 13:14:25.239429 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 13:14:25.239441 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 13:14:25.239454 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.25
I0407 13:14:25.239465 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.5
I0407 13:14:25.239477 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.75
I0407 13:14:25.239488 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 13:14:25.239500 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:14:25.239511 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:14:25.239524 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:14:25.239536 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:14:25.239547 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:14:25.239559 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:14:25.239572 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:14:25.239583 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:14:25.239594 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:14:25.239605 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:14:25.239617 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:14:25.239629 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:14:25.239640 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:14:25.239655 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.20068 (* 0.0454545 = 0.145485 loss)
I0407 13:14:25.239670 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.55099 (* 0.0454545 = 0.161409 loss)
I0407 13:14:25.239683 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.3139 (* 0.0454545 = 0.150632 loss)
I0407 13:14:25.239697 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.5155 (* 0.0454545 = 0.159795 loss)
I0407 13:14:25.239711 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.4291 (* 0.0454545 = 0.155868 loss)
I0407 13:14:25.239724 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.40058 (* 0.0454545 = 0.154572 loss)
I0407 13:14:25.239738 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.5338 (* 0.0454545 = 0.115173 loss)
I0407 13:14:25.239753 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.44952 (* 0.0454545 = 0.0658872 loss)
I0407 13:14:25.239765 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.574276 (* 0.0454545 = 0.0261034 loss)
I0407 13:14:25.239780 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.190023 (* 0.0454545 = 0.00863741 loss)
I0407 13:14:25.239794 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 3.85495e-05 (* 0.0454545 = 1.75225e-06 loss)
I0407 13:14:25.239809 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 3.80893e-05 (* 0.0454545 = 1.73133e-06 loss)
I0407 13:14:25.239822 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.04906e-05 (* 0.0454545 = 1.84048e-06 loss)
I0407 13:14:25.239836 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 3.63495e-05 (* 0.0454545 = 1.65225e-06 loss)
I0407 13:14:25.239850 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.91549e-05 (* 0.0454545 = 1.77977e-06 loss)
I0407 13:14:25.239864 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.72139e-05 (* 0.0454545 = 1.69154e-06 loss)
I0407 13:14:25.239878 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 3.75975e-05 (* 0.0454545 = 1.70898e-06 loss)
I0407 13:14:25.239912 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.03788e-05 (* 0.0454545 = 1.8354e-06 loss)
I0407 13:14:25.239926 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 3.67742e-05 (* 0.0454545 = 1.67156e-06 loss)
I0407 13:14:25.239940 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 3.88737e-05 (* 0.0454545 = 1.76698e-06 loss)
I0407 13:14:25.239953 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 3.80521e-05 (* 0.0454545 = 1.72964e-06 loss)
I0407 13:14:25.239967 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.76888e-05 (* 0.0454545 = 1.71313e-06 loss)
I0407 13:14:25.239979 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:14:25.239991 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000446414
I0407 13:14:25.240005 32304 sgd_solver.cpp:106] Iteration 21500, lr = 0.00957
I0407 13:15:37.242390 32304 solver.cpp:229] Iteration 22000, loss = 1.03683
I0407 13:15:37.242516 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 13:15:37.242537 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:15:37.242549 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 13:15:37.242563 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:15:37.242573 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 13:15:37.242585 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 13:15:37.242597 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.59375
I0407 13:15:37.242609 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 13:15:37.242620 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:15:37.242632 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:15:37.242645 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:15:37.242655 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:15:37.242667 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:15:37.242678 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:15:37.242691 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:15:37.242702 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:15:37.242713 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:15:37.242725 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:15:37.242736 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:15:37.242748 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:15:37.242759 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:15:37.242770 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:15:37.242786 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.66012 (* 0.0454545 = 0.166369 loss)
I0407 13:15:37.242800 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.62016 (* 0.0454545 = 0.164553 loss)
I0407 13:15:37.242815 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.70131 (* 0.0454545 = 0.168241 loss)
I0407 13:15:37.242828 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.59612 (* 0.0454545 = 0.16346 loss)
I0407 13:15:37.242841 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.49064 (* 0.0454545 = 0.158666 loss)
I0407 13:15:37.242856 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.6608 (* 0.0454545 = 0.120945 loss)
I0407 13:15:37.242869 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.04255 (* 0.0454545 = 0.092843 loss)
I0407 13:15:37.242882 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.784398 (* 0.0454545 = 0.0356545 loss)
I0407 13:15:37.242897 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.260894 (* 0.0454545 = 0.0118588 loss)
I0407 13:15:37.242910 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00736546 (* 0.0454545 = 0.000334793 loss)
I0407 13:15:37.242928 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 9.47445e-05 (* 0.0454545 = 4.30657e-06 loss)
I0407 13:15:37.242943 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 9.3605e-05 (* 0.0454545 = 4.25477e-06 loss)
I0407 13:15:37.242956 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 9.77815e-05 (* 0.0454545 = 4.44461e-06 loss)
I0407 13:15:37.242969 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.88392e-05 (* 0.0454545 = 4.03814e-06 loss)
I0407 13:15:37.242983 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 9.55883e-05 (* 0.0454545 = 4.34492e-06 loss)
I0407 13:15:37.242997 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 9.24036e-05 (* 0.0454545 = 4.20017e-06 loss)
I0407 13:15:37.243011 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 9.56145e-05 (* 0.0454545 = 4.34612e-06 loss)
I0407 13:15:37.243042 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 9.90272e-05 (* 0.0454545 = 4.50123e-06 loss)
I0407 13:15:37.243057 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 9.05126e-05 (* 0.0454545 = 4.11421e-06 loss)
I0407 13:15:37.243072 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 9.81752e-05 (* 0.0454545 = 4.46251e-06 loss)
I0407 13:15:37.243085 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 9.71212e-05 (* 0.0454545 = 4.4146e-06 loss)
I0407 13:15:37.243099 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 9.34866e-05 (* 0.0454545 = 4.24939e-06 loss)
I0407 13:15:37.243111 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:15:37.243122 32304 solver.cpp:245]     Train net output #45: total_confidence = 1.41722e-05
I0407 13:15:37.243137 32304 sgd_solver.cpp:106] Iteration 22000, lr = 0.00956
I0407 13:16:49.308630 32304 solver.cpp:229] Iteration 22500, loss = 1.02372
I0407 13:16:49.308823 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 13:16:49.308845 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:16:49.308859 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 13:16:49.308872 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 13:16:49.308884 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 13:16:49.308897 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 13:16:49.308908 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 13:16:49.308923 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 13:16:49.308935 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:16:49.308948 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:16:49.308959 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:16:49.308971 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:16:49.308982 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:16:49.308993 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:16:49.309005 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:16:49.309016 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:16:49.309027 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:16:49.309039 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:16:49.309051 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:16:49.309062 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:16:49.309072 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:16:49.309084 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:16:49.309100 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.6413 (* 0.0454545 = 0.165514 loss)
I0407 13:16:49.309115 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.69438 (* 0.0454545 = 0.167926 loss)
I0407 13:16:49.309128 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.44998 (* 0.0454545 = 0.156817 loss)
I0407 13:16:49.309142 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.66196 (* 0.0454545 = 0.166453 loss)
I0407 13:16:49.309156 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.55881 (* 0.0454545 = 0.161764 loss)
I0407 13:16:49.309170 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.6356 (* 0.0454545 = 0.1198 loss)
I0407 13:16:49.309183 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.28714 (* 0.0454545 = 0.0585064 loss)
I0407 13:16:49.309197 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.769406 (* 0.0454545 = 0.034973 loss)
I0407 13:16:49.309211 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.214245 (* 0.0454545 = 0.00973841 loss)
I0407 13:16:49.309226 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0378356 (* 0.0454545 = 0.0017198 loss)
I0407 13:16:49.309240 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000100981 (* 0.0454545 = 4.59003e-06 loss)
I0407 13:16:49.309257 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000102503 (* 0.0454545 = 4.65922e-06 loss)
I0407 13:16:49.309283 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000107313 (* 0.0454545 = 4.87785e-06 loss)
I0407 13:16:49.309308 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 9.69178e-05 (* 0.0454545 = 4.40535e-06 loss)
I0407 13:16:49.309324 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000102897 (* 0.0454545 = 4.67715e-06 loss)
I0407 13:16:49.309337 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 9.91088e-05 (* 0.0454545 = 4.50494e-06 loss)
I0407 13:16:49.309351 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000101542 (* 0.0454545 = 4.61553e-06 loss)
I0407 13:16:49.309381 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000106468 (* 0.0454545 = 4.83944e-06 loss)
I0407 13:16:49.309396 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 9.78829e-05 (* 0.0454545 = 4.44922e-06 loss)
I0407 13:16:49.309409 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000107401 (* 0.0454545 = 4.88188e-06 loss)
I0407 13:16:49.309423 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000103135 (* 0.0454545 = 4.68795e-06 loss)
I0407 13:16:49.309437 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 9.78449e-05 (* 0.0454545 = 4.4475e-06 loss)
I0407 13:16:49.309453 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:16:49.309465 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000139507
I0407 13:16:49.309480 32304 sgd_solver.cpp:106] Iteration 22500, lr = 0.00955
I0407 13:18:01.458328 32304 solver.cpp:229] Iteration 23000, loss = 1.02853
I0407 13:18:01.458484 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:18:01.458504 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:18:01.458518 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:18:01.458530 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.1875
I0407 13:18:01.458542 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 13:18:01.458554 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 13:18:01.458566 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 13:18:01.458577 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.8125
I0407 13:18:01.458590 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:18:01.458601 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:18:01.458613 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:18:01.458626 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:18:01.458637 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:18:01.458648 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:18:01.458660 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:18:01.458672 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:18:01.458683 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:18:01.458693 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:18:01.458704 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:18:01.458716 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:18:01.458736 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:18:01.458747 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:18:01.458762 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.50801 (* 0.0454545 = 0.159455 loss)
I0407 13:18:01.458777 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.53313 (* 0.0454545 = 0.160597 loss)
I0407 13:18:01.458799 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.61154 (* 0.0454545 = 0.164161 loss)
I0407 13:18:01.458812 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.16777 (* 0.0454545 = 0.143989 loss)
I0407 13:18:01.458827 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.98736 (* 0.0454545 = 0.135789 loss)
I0407 13:18:01.458840 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.61498 (* 0.0454545 = 0.118863 loss)
I0407 13:18:01.458863 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.84497 (* 0.0454545 = 0.0838622 loss)
I0407 13:18:01.458876 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.930319 (* 0.0454545 = 0.0422872 loss)
I0407 13:18:01.458890 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.420145 (* 0.0454545 = 0.0190975 loss)
I0407 13:18:01.458904 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.188205 (* 0.0454545 = 0.00855479 loss)
I0407 13:18:01.458930 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.0001126 (* 0.0454545 = 5.11816e-06 loss)
I0407 13:18:01.458945 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000123021 (* 0.0454545 = 5.59188e-06 loss)
I0407 13:18:01.458959 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.00011103 (* 0.0454545 = 5.04681e-06 loss)
I0407 13:18:01.458974 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.0001115 (* 0.0454545 = 5.0682e-06 loss)
I0407 13:18:01.458988 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000124273 (* 0.0454545 = 5.64878e-06 loss)
I0407 13:18:01.459002 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000112088 (* 0.0454545 = 5.09492e-06 loss)
I0407 13:18:01.459017 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000105888 (* 0.0454545 = 4.81309e-06 loss)
I0407 13:18:01.459049 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000132025 (* 0.0454545 = 6.00113e-06 loss)
I0407 13:18:01.459065 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000110832 (* 0.0454545 = 5.03784e-06 loss)
I0407 13:18:01.459079 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000121565 (* 0.0454545 = 5.52569e-06 loss)
I0407 13:18:01.459092 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000112168 (* 0.0454545 = 5.09854e-06 loss)
I0407 13:18:01.459106 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000114407 (* 0.0454545 = 5.20033e-06 loss)
I0407 13:18:01.459118 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:18:01.459131 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000549973
I0407 13:18:01.459146 32304 sgd_solver.cpp:106] Iteration 23000, lr = 0.00954
I0407 13:19:13.318948 32304 solver.cpp:229] Iteration 23500, loss = 1.02289
I0407 13:19:13.319069 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:19:13.319092 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:19:13.319104 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 13:19:13.319116 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:19:13.319128 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 13:19:13.319140 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 13:19:13.319152 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.59375
I0407 13:19:13.319164 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.8125
I0407 13:19:13.319176 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 13:19:13.319188 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.90625
I0407 13:19:13.319200 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:19:13.319211 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:19:13.319223 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:19:13.319234 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:19:13.319245 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:19:13.319257 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:19:13.319268 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:19:13.319279 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:19:13.319291 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:19:13.319303 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:19:13.319314 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:19:13.319339 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:19:13.319356 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.51316 (* 0.0454545 = 0.159689 loss)
I0407 13:19:13.319371 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.60498 (* 0.0454545 = 0.163863 loss)
I0407 13:19:13.319385 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.62557 (* 0.0454545 = 0.164799 loss)
I0407 13:19:13.319398 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.51034 (* 0.0454545 = 0.159561 loss)
I0407 13:19:13.319411 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.5338 (* 0.0454545 = 0.160627 loss)
I0407 13:19:13.319425 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.70442 (* 0.0454545 = 0.122928 loss)
I0407 13:19:13.319439 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.0637 (* 0.0454545 = 0.0938044 loss)
I0407 13:19:13.319458 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.851031 (* 0.0454545 = 0.0386832 loss)
I0407 13:19:13.319473 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.402976 (* 0.0454545 = 0.0183171 loss)
I0407 13:19:13.319486 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.471666 (* 0.0454545 = 0.0214393 loss)
I0407 13:19:13.319501 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 9.14532e-05 (* 0.0454545 = 4.15697e-06 loss)
I0407 13:19:13.319515 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 9.34949e-05 (* 0.0454545 = 4.24977e-06 loss)
I0407 13:19:13.319530 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000104455 (* 0.0454545 = 4.74797e-06 loss)
I0407 13:19:13.319543 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.84368e-05 (* 0.0454545 = 4.01985e-06 loss)
I0407 13:19:13.319558 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 9.28659e-05 (* 0.0454545 = 4.22118e-06 loss)
I0407 13:19:13.319572 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 9.25341e-05 (* 0.0454545 = 4.20609e-06 loss)
I0407 13:19:13.319586 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 9.4681e-05 (* 0.0454545 = 4.30368e-06 loss)
I0407 13:19:13.319619 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 9.02718e-05 (* 0.0454545 = 4.10326e-06 loss)
I0407 13:19:13.319634 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 9.2807e-05 (* 0.0454545 = 4.2185e-06 loss)
I0407 13:19:13.319648 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 9.71346e-05 (* 0.0454545 = 4.41521e-06 loss)
I0407 13:19:13.319663 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 9.4537e-05 (* 0.0454545 = 4.29713e-06 loss)
I0407 13:19:13.319677 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 8.74206e-05 (* 0.0454545 = 3.97366e-06 loss)
I0407 13:19:13.319689 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:19:13.319701 32304 solver.cpp:245]     Train net output #45: total_confidence = 4.01709e-05
I0407 13:19:13.319715 32304 sgd_solver.cpp:106] Iteration 23500, lr = 0.00953
I0407 13:20:25.209117 32304 solver.cpp:229] Iteration 24000, loss = 1.01323
I0407 13:20:25.209269 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:20:25.209290 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:20:25.209303 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:20:25.209316 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 13:20:25.209328 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.34375
I0407 13:20:25.209341 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5
I0407 13:20:25.209352 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:20:25.209364 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 13:20:25.209377 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:20:25.209388 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:20:25.209399 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:20:25.209410 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:20:25.209422 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:20:25.209434 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:20:25.209445 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:20:25.209457 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:20:25.209468 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:20:25.209480 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:20:25.209491 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:20:25.209501 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:20:25.209513 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:20:25.209524 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:20:25.209540 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.06491 (* 0.0454545 = 0.139314 loss)
I0407 13:20:25.209555 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.43759 (* 0.0454545 = 0.156254 loss)
I0407 13:20:25.209568 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.38337 (* 0.0454545 = 0.15379 loss)
I0407 13:20:25.209583 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.39538 (* 0.0454545 = 0.154335 loss)
I0407 13:20:25.209601 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.69657 (* 0.0454545 = 0.122571 loss)
I0407 13:20:25.209630 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.10419 (* 0.0454545 = 0.0956449 loss)
I0407 13:20:25.209655 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.43878 (* 0.0454545 = 0.0653992 loss)
I0407 13:20:25.209681 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.227054 (* 0.0454545 = 0.0103206 loss)
I0407 13:20:25.209704 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0253408 (* 0.0454545 = 0.00115186 loss)
I0407 13:20:25.209728 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00908488 (* 0.0454545 = 0.000412949 loss)
I0407 13:20:25.209753 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 3.67949e-05 (* 0.0454545 = 1.6725e-06 loss)
I0407 13:20:25.209776 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.45565e-05 (* 0.0454545 = 2.0253e-06 loss)
I0407 13:20:25.209800 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.02694e-05 (* 0.0454545 = 1.83043e-06 loss)
I0407 13:20:25.209822 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 3.6974e-05 (* 0.0454545 = 1.68064e-06 loss)
I0407 13:20:25.209846 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.84161e-05 (* 0.0454545 = 1.74619e-06 loss)
I0407 13:20:25.209869 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.71456e-05 (* 0.0454545 = 1.68843e-06 loss)
I0407 13:20:25.209893 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 3.74898e-05 (* 0.0454545 = 1.70408e-06 loss)
I0407 13:20:25.209942 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 3.94632e-05 (* 0.0454545 = 1.79378e-06 loss)
I0407 13:20:25.209967 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 3.58614e-05 (* 0.0454545 = 1.63007e-06 loss)
I0407 13:20:25.209991 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.23163e-05 (* 0.0454545 = 1.92347e-06 loss)
I0407 13:20:25.210014 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 3.96269e-05 (* 0.0454545 = 1.80122e-06 loss)
I0407 13:20:25.210039 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.75105e-05 (* 0.0454545 = 1.70502e-06 loss)
I0407 13:20:25.210059 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:20:25.210078 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000291219
I0407 13:20:25.210100 32304 sgd_solver.cpp:106] Iteration 24000, lr = 0.00952
I0407 13:21:37.615422 32304 solver.cpp:229] Iteration 24500, loss = 1.01255
I0407 13:21:37.615607 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:21:37.615629 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 13:21:37.615643 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:21:37.615655 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 13:21:37.615667 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.09375
I0407 13:21:37.615679 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 13:21:37.615690 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.84375
I0407 13:21:37.615702 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:21:37.615715 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:21:37.615726 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:21:37.615737 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:21:37.615749 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:21:37.615761 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:21:37.615773 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:21:37.615784 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:21:37.615795 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:21:37.615808 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:21:37.615818 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:21:37.615829 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:21:37.615840 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:21:37.615852 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:21:37.615864 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:21:37.615880 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.38196 (* 0.0454545 = 0.153725 loss)
I0407 13:21:37.615893 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.43986 (* 0.0454545 = 0.156357 loss)
I0407 13:21:37.615907 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.4043 (* 0.0454545 = 0.154741 loss)
I0407 13:21:37.615923 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.58306 (* 0.0454545 = 0.162866 loss)
I0407 13:21:37.615937 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.50431 (* 0.0454545 = 0.159287 loss)
I0407 13:21:37.615952 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.70558 (* 0.0454545 = 0.122981 loss)
I0407 13:21:37.615965 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.985227 (* 0.0454545 = 0.044783 loss)
I0407 13:21:37.615979 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.530126 (* 0.0454545 = 0.0240967 loss)
I0407 13:21:37.615993 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.27065 (* 0.0454545 = 0.0123023 loss)
I0407 13:21:37.616008 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0137747 (* 0.0454545 = 0.000626124 loss)
I0407 13:21:37.616022 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 3.3935e-05 (* 0.0454545 = 1.5425e-06 loss)
I0407 13:21:37.616037 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 3.88154e-05 (* 0.0454545 = 1.76434e-06 loss)
I0407 13:21:37.616051 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.13713e-05 (* 0.0454545 = 1.88051e-06 loss)
I0407 13:21:37.616065 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 3.6636e-05 (* 0.0454545 = 1.66527e-06 loss)
I0407 13:21:37.616080 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.57678e-05 (* 0.0454545 = 1.62581e-06 loss)
I0407 13:21:37.616093 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.43224e-05 (* 0.0454545 = 1.56011e-06 loss)
I0407 13:21:37.616107 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 3.83982e-05 (* 0.0454545 = 1.74537e-06 loss)
I0407 13:21:37.616135 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 3.58871e-05 (* 0.0454545 = 1.63123e-06 loss)
I0407 13:21:37.616150 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 3.6554e-05 (* 0.0454545 = 1.66155e-06 loss)
I0407 13:21:37.616164 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 3.79362e-05 (* 0.0454545 = 1.72437e-06 loss)
I0407 13:21:37.616178 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 3.81971e-05 (* 0.0454545 = 1.73623e-06 loss)
I0407 13:21:37.616191 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.77201e-05 (* 0.0454545 = 1.71455e-06 loss)
I0407 13:21:37.616204 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:21:37.616215 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000121226
I0407 13:21:37.616241 32304 sgd_solver.cpp:106] Iteration 24500, lr = 0.00951
I0407 13:22:49.690958 32304 solver.cpp:338] Iteration 25000, Testing net (#0)
I0407 13:22:57.726039 32304 solver.cpp:393] Test loss: 0.99194
I0407 13:22:57.726102 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.104
I0407 13:22:57.726119 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.054
I0407 13:22:57.726131 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.068
I0407 13:22:57.726143 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.096
I0407 13:22:57.726155 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.203
I0407 13:22:57.726166 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.488
I0407 13:22:57.726178 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.893
I0407 13:22:57.726189 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 13:22:57.726200 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 13:22:57.726212 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 13:22:57.726223 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 13:22:57.726234 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 13:22:57.726245 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 13:22:57.726258 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 13:22:57.726269 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 13:22:57.726279 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 13:22:57.726290 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 13:22:57.726301 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 13:22:57.726312 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 13:22:57.726323 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 13:22:57.726335 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 13:22:57.726346 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 13:22:57.726361 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 3.69973 (* 0.0454545 = 0.168169 loss)
I0407 13:22:57.726375 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.76164 (* 0.0454545 = 0.170984 loss)
I0407 13:22:57.726388 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 3.69009 (* 0.0454545 = 0.167731 loss)
I0407 13:22:57.726402 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 3.62941 (* 0.0454545 = 0.164973 loss)
I0407 13:22:57.726415 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 3.3769 (* 0.0454545 = 0.153496 loss)
I0407 13:22:57.726429 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 2.43767 (* 0.0454545 = 0.110803 loss)
I0407 13:22:57.726443 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.848177 (* 0.0454545 = 0.0385535 loss)
I0407 13:22:57.726455 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.276639 (* 0.0454545 = 0.0125745 loss)
I0407 13:22:57.726469 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0689382 (* 0.0454545 = 0.00313356 loss)
I0407 13:22:57.726483 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0305771 (* 0.0454545 = 0.00138987 loss)
I0407 13:22:57.726497 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 0.000231318 (* 0.0454545 = 1.05144e-05 loss)
I0407 13:22:57.726511 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 0.000251525 (* 0.0454545 = 1.14329e-05 loss)
I0407 13:22:57.726524 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 0.000263892 (* 0.0454545 = 1.19951e-05 loss)
I0407 13:22:57.726537 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 0.000238452 (* 0.0454545 = 1.08387e-05 loss)
I0407 13:22:57.726552 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 0.000235261 (* 0.0454545 = 1.06937e-05 loss)
I0407 13:22:57.726564 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 0.000229597 (* 0.0454545 = 1.04362e-05 loss)
I0407 13:22:57.726578 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 0.000249063 (* 0.0454545 = 1.13211e-05 loss)
I0407 13:22:57.726630 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 0.000240062 (* 0.0454545 = 1.09119e-05 loss)
I0407 13:22:57.726645 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 0.000245767 (* 0.0454545 = 1.11712e-05 loss)
I0407 13:22:57.726660 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 0.000254883 (* 0.0454545 = 1.15856e-05 loss)
I0407 13:22:57.726673 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 0.000250086 (* 0.0454545 = 1.13676e-05 loss)
I0407 13:22:57.726686 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 0.000248612 (* 0.0454545 = 1.13006e-05 loss)
I0407 13:22:57.726698 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0
I0407 13:22:57.726709 32304 solver.cpp:406]     Test net output #45: total_confidence = 0.000123411
I0407 13:22:57.760888 32304 solver.cpp:229] Iteration 25000, loss = 1.00302
I0407 13:22:57.760951 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 13:22:57.760968 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:22:57.760982 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 13:22:57.760994 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 13:22:57.761006 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 13:22:57.761018 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 13:22:57.761030 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 13:22:57.761042 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 13:22:57.761054 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:22:57.761065 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:22:57.761080 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:22:57.761092 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:22:57.761104 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:22:57.761116 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:22:57.761127 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:22:57.761138 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:22:57.761150 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:22:57.761162 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:22:57.761173 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:22:57.761184 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:22:57.761195 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:22:57.761207 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:22:57.761222 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.8001 (* 0.0454545 = 0.172732 loss)
I0407 13:22:57.761237 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.80302 (* 0.0454545 = 0.172864 loss)
I0407 13:22:57.761251 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.7124 (* 0.0454545 = 0.168745 loss)
I0407 13:22:57.761265 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.71417 (* 0.0454545 = 0.168826 loss)
I0407 13:22:57.761278 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.44978 (* 0.0454545 = 0.156808 loss)
I0407 13:22:57.761292 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.67017 (* 0.0454545 = 0.121371 loss)
I0407 13:22:57.761306 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.20918 (* 0.0454545 = 0.0549629 loss)
I0407 13:22:57.761319 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.318289 (* 0.0454545 = 0.0144677 loss)
I0407 13:22:57.761339 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.188808 (* 0.0454545 = 0.0085822 loss)
I0407 13:22:57.761353 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0226767 (* 0.0454545 = 0.00103076 loss)
I0407 13:22:57.761394 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 2.63151e-05 (* 0.0454545 = 1.19614e-06 loss)
I0407 13:22:57.761409 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 3.20342e-05 (* 0.0454545 = 1.4561e-06 loss)
I0407 13:22:57.761423 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 3.21646e-05 (* 0.0454545 = 1.46203e-06 loss)
I0407 13:22:57.761437 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 2.77234e-05 (* 0.0454545 = 1.26015e-06 loss)
I0407 13:22:57.761451 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.01174e-05 (* 0.0454545 = 1.36897e-06 loss)
I0407 13:22:57.761466 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 2.72801e-05 (* 0.0454545 = 1.24e-06 loss)
I0407 13:22:57.761479 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 3.01805e-05 (* 0.0454545 = 1.37184e-06 loss)
I0407 13:22:57.761493 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 3.14439e-05 (* 0.0454545 = 1.42927e-06 loss)
I0407 13:22:57.761507 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 2.82263e-05 (* 0.0454545 = 1.28301e-06 loss)
I0407 13:22:57.761521 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 3.34409e-05 (* 0.0454545 = 1.52004e-06 loss)
I0407 13:22:57.761535 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 2.97372e-05 (* 0.0454545 = 1.35169e-06 loss)
I0407 13:22:57.761549 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.144e-05 (* 0.0454545 = 1.42909e-06 loss)
I0407 13:22:57.761561 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:22:57.761572 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000129044
I0407 13:22:57.761587 32304 sgd_solver.cpp:106] Iteration 25000, lr = 0.0095
I0407 13:24:10.289223 32304 solver.cpp:229] Iteration 25500, loss = 0.993754
I0407 13:24:10.289366 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.15625
I0407 13:24:10.289386 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 13:24:10.289400 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 13:24:10.289412 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:24:10.289424 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 13:24:10.289436 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 13:24:10.289448 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:24:10.289459 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:24:10.289471 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:24:10.289484 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:24:10.289495 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:24:10.289506 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:24:10.289517 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:24:10.289530 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:24:10.289541 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:24:10.289552 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:24:10.289563 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:24:10.289574 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:24:10.289585 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:24:10.289597 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:24:10.289608 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:24:10.289619 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:24:10.289634 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.3195 (* 0.0454545 = 0.150886 loss)
I0407 13:24:10.289649 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.638 (* 0.0454545 = 0.165364 loss)
I0407 13:24:10.289662 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.41261 (* 0.0454545 = 0.155119 loss)
I0407 13:24:10.289676 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.40627 (* 0.0454545 = 0.154831 loss)
I0407 13:24:10.289690 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.31182 (* 0.0454545 = 0.150537 loss)
I0407 13:24:10.289703 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.81513 (* 0.0454545 = 0.12796 loss)
I0407 13:24:10.289717 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.37769 (* 0.0454545 = 0.0626225 loss)
I0407 13:24:10.289734 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.535179 (* 0.0454545 = 0.0243263 loss)
I0407 13:24:10.289749 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.180635 (* 0.0454545 = 0.0082107 loss)
I0407 13:24:10.289762 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.195268 (* 0.0454545 = 0.0088758 loss)
I0407 13:24:10.289777 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.34422e-05 (* 0.0454545 = 1.97465e-06 loss)
I0407 13:24:10.289791 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 5.19839e-05 (* 0.0454545 = 2.3629e-06 loss)
I0407 13:24:10.289806 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.14078e-05 (* 0.0454545 = 2.33672e-06 loss)
I0407 13:24:10.289820 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.62963e-05 (* 0.0454545 = 2.10438e-06 loss)
I0407 13:24:10.289834 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.60055e-05 (* 0.0454545 = 2.09116e-06 loss)
I0407 13:24:10.289847 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.53425e-05 (* 0.0454545 = 2.06102e-06 loss)
I0407 13:24:10.289861 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.96103e-05 (* 0.0454545 = 2.25502e-06 loss)
I0407 13:24:10.289892 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.82915e-05 (* 0.0454545 = 2.19507e-06 loss)
I0407 13:24:10.289907 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.78201e-05 (* 0.0454545 = 2.17364e-06 loss)
I0407 13:24:10.289924 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 5.33291e-05 (* 0.0454545 = 2.42405e-06 loss)
I0407 13:24:10.289939 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.91594e-05 (* 0.0454545 = 2.23452e-06 loss)
I0407 13:24:10.289952 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.96905e-05 (* 0.0454545 = 2.25866e-06 loss)
I0407 13:24:10.289964 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:24:10.289975 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000211766
I0407 13:24:10.289990 32304 sgd_solver.cpp:106] Iteration 25500, lr = 0.00949
I0407 13:25:22.879989 32304 solver.cpp:229] Iteration 26000, loss = 0.986286
I0407 13:25:22.880182 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 13:25:22.880203 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:25:22.880215 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:25:22.880228 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:25:22.880239 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 13:25:22.880251 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 13:25:22.880264 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 13:25:22.880275 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.78125
I0407 13:25:22.880286 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:25:22.880298 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:25:22.880309 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:25:22.880321 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:25:22.880332 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:25:22.880344 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:25:22.880357 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:25:22.880367 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:25:22.880378 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:25:22.880390 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:25:22.880401 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:25:22.880412 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:25:22.880424 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:25:22.880435 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:25:22.880451 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.30216 (* 0.0454545 = 0.150098 loss)
I0407 13:25:22.880465 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.41773 (* 0.0454545 = 0.155351 loss)
I0407 13:25:22.880480 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.32597 (* 0.0454545 = 0.151181 loss)
I0407 13:25:22.880492 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.25263 (* 0.0454545 = 0.147847 loss)
I0407 13:25:22.880506 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.09153 (* 0.0454545 = 0.140524 loss)
I0407 13:25:22.880520 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.60405 (* 0.0454545 = 0.118366 loss)
I0407 13:25:22.880534 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.09939 (* 0.0454545 = 0.0954266 loss)
I0407 13:25:22.880548 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.23421 (* 0.0454545 = 0.0561005 loss)
I0407 13:25:22.880561 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.386559 (* 0.0454545 = 0.0175708 loss)
I0407 13:25:22.880575 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0185425 (* 0.0454545 = 0.00084284 loss)
I0407 13:25:22.880589 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 6.73443e-05 (* 0.0454545 = 3.06111e-06 loss)
I0407 13:25:22.880604 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.94077e-05 (* 0.0454545 = 3.60944e-06 loss)
I0407 13:25:22.880617 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.56348e-05 (* 0.0454545 = 3.43794e-06 loss)
I0407 13:25:22.880631 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.49743e-05 (* 0.0454545 = 2.95338e-06 loss)
I0407 13:25:22.880645 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.49458e-05 (* 0.0454545 = 3.40663e-06 loss)
I0407 13:25:22.880659 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.91778e-05 (* 0.0454545 = 3.14444e-06 loss)
I0407 13:25:22.880672 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 7.41668e-05 (* 0.0454545 = 3.37122e-06 loss)
I0407 13:25:22.880702 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.85697e-05 (* 0.0454545 = 3.57135e-06 loss)
I0407 13:25:22.880717 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.81121e-05 (* 0.0454545 = 3.096e-06 loss)
I0407 13:25:22.880730 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 8.30527e-05 (* 0.0454545 = 3.77512e-06 loss)
I0407 13:25:22.880744 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.62485e-05 (* 0.0454545 = 3.0113e-06 loss)
I0407 13:25:22.880759 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.41294e-05 (* 0.0454545 = 3.36952e-06 loss)
I0407 13:25:22.880769 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:25:22.880781 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000433173
I0407 13:25:22.880795 32304 sgd_solver.cpp:106] Iteration 26000, lr = 0.00948
I0407 13:26:35.368341 32304 solver.cpp:229] Iteration 26500, loss = 0.98321
I0407 13:26:35.368439 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0
I0407 13:26:35.368458 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:26:35.368471 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.15625
I0407 13:26:35.368484 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.1875
I0407 13:26:35.368496 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.375
I0407 13:26:35.368508 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5625
I0407 13:26:35.368520 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 13:26:35.368531 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:26:35.368542 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:26:35.368554 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:26:35.368566 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:26:35.368577 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:26:35.368588 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:26:35.368599 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:26:35.368610 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:26:35.368621 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:26:35.368633 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:26:35.368644 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:26:35.368656 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:26:35.368667 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:26:35.368680 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:26:35.368691 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:26:35.368707 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.24149 (* 0.0454545 = 0.147341 loss)
I0407 13:26:35.368721 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.24262 (* 0.0454545 = 0.147392 loss)
I0407 13:26:35.368736 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.21302 (* 0.0454545 = 0.146046 loss)
I0407 13:26:35.368749 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.01062 (* 0.0454545 = 0.136846 loss)
I0407 13:26:35.368762 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.73561 (* 0.0454545 = 0.124346 loss)
I0407 13:26:35.368780 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.17396 (* 0.0454545 = 0.0988165 loss)
I0407 13:26:35.368794 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.94158 (* 0.0454545 = 0.0427991 loss)
I0407 13:26:35.368808 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.385728 (* 0.0454545 = 0.0175331 loss)
I0407 13:26:35.368823 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.178235 (* 0.0454545 = 0.00810158 loss)
I0407 13:26:35.368835 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00831369 (* 0.0454545 = 0.000377895 loss)
I0407 13:26:35.368850 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 9.21412e-05 (* 0.0454545 = 4.18824e-06 loss)
I0407 13:26:35.368863 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000108491 (* 0.0454545 = 4.9314e-06 loss)
I0407 13:26:35.368877 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 9.73857e-05 (* 0.0454545 = 4.42662e-06 loss)
I0407 13:26:35.368891 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.82243e-05 (* 0.0454545 = 4.0102e-06 loss)
I0407 13:26:35.368906 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 9.64185e-05 (* 0.0454545 = 4.38266e-06 loss)
I0407 13:26:35.368918 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000105032 (* 0.0454545 = 4.77419e-06 loss)
I0407 13:26:35.368932 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000101879 (* 0.0454545 = 4.63087e-06 loss)
I0407 13:26:35.368963 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000121503 (* 0.0454545 = 5.52285e-06 loss)
I0407 13:26:35.368978 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 9.08957e-05 (* 0.0454545 = 4.13162e-06 loss)
I0407 13:26:35.368993 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000130542 (* 0.0454545 = 5.93371e-06 loss)
I0407 13:26:35.369006 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000100264 (* 0.0454545 = 4.55745e-06 loss)
I0407 13:26:35.369019 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000103502 (* 0.0454545 = 4.70462e-06 loss)
I0407 13:26:35.369031 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:26:35.369043 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000331798
I0407 13:26:35.369057 32304 sgd_solver.cpp:106] Iteration 26500, lr = 0.00947
I0407 13:27:48.037508 32304 solver.cpp:229] Iteration 27000, loss = 0.975591
I0407 13:27:48.037631 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:27:48.037650 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:27:48.037664 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 13:27:48.037677 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:27:48.037688 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 13:27:48.037700 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 13:27:48.037713 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:27:48.037724 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:27:48.037736 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:27:48.037747 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:27:48.037760 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:27:48.037771 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:27:48.037782 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:27:48.037794 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:27:48.037806 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:27:48.037817 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:27:48.037828 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:27:48.037839 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:27:48.037850 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:27:48.037861 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:27:48.037873 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:27:48.037884 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:27:48.037900 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.51124 (* 0.0454545 = 0.159602 loss)
I0407 13:27:48.037914 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.39601 (* 0.0454545 = 0.154364 loss)
I0407 13:27:48.037931 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.52555 (* 0.0454545 = 0.160252 loss)
I0407 13:27:48.037945 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.39038 (* 0.0454545 = 0.154108 loss)
I0407 13:27:48.037960 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.2946 (* 0.0454545 = 0.149755 loss)
I0407 13:27:48.037973 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.5596 (* 0.0454545 = 0.116345 loss)
I0407 13:27:48.037986 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.186 (* 0.0454545 = 0.053909 loss)
I0407 13:27:48.038000 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.293614 (* 0.0454545 = 0.0133461 loss)
I0407 13:27:48.038014 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.203128 (* 0.0454545 = 0.00923308 loss)
I0407 13:27:48.038028 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.193726 (* 0.0454545 = 0.00880571 loss)
I0407 13:27:48.038043 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 3.47277e-05 (* 0.0454545 = 1.57853e-06 loss)
I0407 13:27:48.038056 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 3.84271e-05 (* 0.0454545 = 1.74669e-06 loss)
I0407 13:27:48.038070 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.07967e-05 (* 0.0454545 = 1.8544e-06 loss)
I0407 13:27:48.038084 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 3.89083e-05 (* 0.0454545 = 1.76856e-06 loss)
I0407 13:27:48.038099 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.1491e-05 (* 0.0454545 = 1.43141e-06 loss)
I0407 13:27:48.038112 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.66814e-05 (* 0.0454545 = 1.66734e-06 loss)
I0407 13:27:48.038125 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.18015e-05 (* 0.0454545 = 1.90007e-06 loss)
I0407 13:27:48.038157 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 3.72261e-05 (* 0.0454545 = 1.6921e-06 loss)
I0407 13:27:48.038172 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 3.8622e-05 (* 0.0454545 = 1.75555e-06 loss)
I0407 13:27:48.038187 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.07722e-05 (* 0.0454545 = 1.85328e-06 loss)
I0407 13:27:48.038200 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.37633e-05 (* 0.0454545 = 1.98924e-06 loss)
I0407 13:27:48.038215 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.59677e-05 (* 0.0454545 = 1.6349e-06 loss)
I0407 13:27:48.038226 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:27:48.038239 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000191768
I0407 13:27:48.038254 32304 sgd_solver.cpp:106] Iteration 27000, lr = 0.00946
I0407 13:29:00.652282 32304 solver.cpp:229] Iteration 27500, loss = 0.97062
I0407 13:29:00.652410 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:29:00.652431 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:29:00.652443 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:29:00.652456 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:29:00.652467 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 13:29:00.652479 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.46875
I0407 13:29:00.652492 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 13:29:00.652503 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:29:00.652515 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:29:00.652526 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:29:00.652539 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:29:00.652549 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:29:00.652561 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:29:00.652573 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:29:00.652585 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:29:00.652596 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:29:00.652607 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:29:00.652619 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:29:00.652631 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:29:00.652642 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:29:00.652653 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:29:00.652664 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:29:00.652680 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.08412 (* 0.0454545 = 0.140187 loss)
I0407 13:29:00.652694 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.42185 (* 0.0454545 = 0.155539 loss)
I0407 13:29:00.652709 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.47124 (* 0.0454545 = 0.157784 loss)
I0407 13:29:00.652722 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.33021 (* 0.0454545 = 0.151373 loss)
I0407 13:29:00.652735 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.95927 (* 0.0454545 = 0.134512 loss)
I0407 13:29:00.652750 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.64413 (* 0.0454545 = 0.120188 loss)
I0407 13:29:00.652763 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.24673 (* 0.0454545 = 0.0566696 loss)
I0407 13:29:00.652776 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.515057 (* 0.0454545 = 0.0234117 loss)
I0407 13:29:00.652791 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.31669 (* 0.0454545 = 0.014395 loss)
I0407 13:29:00.652806 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0112899 (* 0.0454545 = 0.000513179 loss)
I0407 13:29:00.652819 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 2.21762e-05 (* 0.0454545 = 1.00801e-06 loss)
I0407 13:29:00.652833 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 2.32696e-05 (* 0.0454545 = 1.05771e-06 loss)
I0407 13:29:00.652848 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 2.37168e-05 (* 0.0454545 = 1.07804e-06 loss)
I0407 13:29:00.652860 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 2.21317e-05 (* 0.0454545 = 1.00599e-06 loss)
I0407 13:29:00.652874 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 2.25338e-05 (* 0.0454545 = 1.02426e-06 loss)
I0407 13:29:00.652889 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 2.28675e-05 (* 0.0454545 = 1.03943e-06 loss)
I0407 13:29:00.652902 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 2.29213e-05 (* 0.0454545 = 1.04188e-06 loss)
I0407 13:29:00.652936 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 2.36049e-05 (* 0.0454545 = 1.07295e-06 loss)
I0407 13:29:00.652952 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 2.3292e-05 (* 0.0454545 = 1.05873e-06 loss)
I0407 13:29:00.652966 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 2.65113e-05 (* 0.0454545 = 1.20506e-06 loss)
I0407 13:29:00.652981 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 2.33724e-05 (* 0.0454545 = 1.06238e-06 loss)
I0407 13:29:00.652993 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 2.41787e-05 (* 0.0454545 = 1.09903e-06 loss)
I0407 13:29:00.653005 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:29:00.653017 32304 solver.cpp:245]     Train net output #45: total_confidence = 9.0066e-05
I0407 13:29:00.653031 32304 sgd_solver.cpp:106] Iteration 27500, lr = 0.00945
I0407 13:30:12.959508 32304 solver.cpp:229] Iteration 28000, loss = 0.964748
I0407 13:30:12.959671 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 13:30:12.959692 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.21875
I0407 13:30:12.959704 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:30:12.959717 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:30:12.959728 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.3125
I0407 13:30:12.959740 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 13:30:12.959753 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 13:30:12.959764 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:30:12.959776 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:30:12.959789 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:30:12.959800 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:30:12.959811 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:30:12.959823 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:30:12.959835 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:30:12.959846 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:30:12.959858 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:30:12.959869 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:30:12.959882 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:30:12.959892 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:30:12.959903 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:30:12.959915 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:30:12.959929 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:30:12.959945 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.14164 (* 0.0454545 = 0.142802 loss)
I0407 13:30:12.959960 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.06028 (* 0.0454545 = 0.139104 loss)
I0407 13:30:12.959975 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.35158 (* 0.0454545 = 0.152344 loss)
I0407 13:30:12.959988 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.27331 (* 0.0454545 = 0.148787 loss)
I0407 13:30:12.960002 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.94709 (* 0.0454545 = 0.133959 loss)
I0407 13:30:12.960016 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.54417 (* 0.0454545 = 0.115644 loss)
I0407 13:30:12.960029 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.44762 (* 0.0454545 = 0.0658008 loss)
I0407 13:30:12.960042 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.460368 (* 0.0454545 = 0.0209258 loss)
I0407 13:30:12.960057 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.214261 (* 0.0454545 = 0.00973913 loss)
I0407 13:30:12.960072 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0147743 (* 0.0454545 = 0.000671559 loss)
I0407 13:30:12.960086 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.27172e-05 (* 0.0454545 = 3.75987e-06 loss)
I0407 13:30:12.960100 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 9.04408e-05 (* 0.0454545 = 4.11095e-06 loss)
I0407 13:30:12.960114 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 8.68564e-05 (* 0.0454545 = 3.94802e-06 loss)
I0407 13:30:12.960127 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.12321e-05 (* 0.0454545 = 3.69237e-06 loss)
I0407 13:30:12.960141 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 8.5055e-05 (* 0.0454545 = 3.86614e-06 loss)
I0407 13:30:12.960156 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 8.22663e-05 (* 0.0454545 = 3.73938e-06 loss)
I0407 13:30:12.960170 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 8.85744e-05 (* 0.0454545 = 4.02611e-06 loss)
I0407 13:30:12.960196 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 8.38797e-05 (* 0.0454545 = 3.81271e-06 loss)
I0407 13:30:12.960216 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 8.82313e-05 (* 0.0454545 = 4.01051e-06 loss)
I0407 13:30:12.960230 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 9.10634e-05 (* 0.0454545 = 4.13925e-06 loss)
I0407 13:30:12.960244 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 8.56422e-05 (* 0.0454545 = 3.89283e-06 loss)
I0407 13:30:12.960258 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 8.74416e-05 (* 0.0454545 = 3.97462e-06 loss)
I0407 13:30:12.960269 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:30:12.960281 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000169566
I0407 13:30:12.960296 32304 sgd_solver.cpp:106] Iteration 28000, lr = 0.00944
I0407 13:31:25.750957 32304 solver.cpp:229] Iteration 28500, loss = 0.957366
I0407 13:31:25.751101 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:31:25.751119 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.15625
I0407 13:31:25.751132 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:31:25.751145 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.21875
I0407 13:31:25.751157 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 13:31:25.751169 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 13:31:25.751181 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 13:31:25.751194 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:31:25.751205 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:31:25.751219 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:31:25.751230 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:31:25.751241 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:31:25.751253 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:31:25.751265 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:31:25.751276 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:31:25.751287 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:31:25.751298 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:31:25.751310 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:31:25.751341 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:31:25.751355 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:31:25.751368 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:31:25.751379 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:31:25.751395 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.96089 (* 0.0454545 = 0.134586 loss)
I0407 13:31:25.751410 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.18128 (* 0.0454545 = 0.144604 loss)
I0407 13:31:25.751423 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.2089 (* 0.0454545 = 0.145859 loss)
I0407 13:31:25.751437 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.17222 (* 0.0454545 = 0.144192 loss)
I0407 13:31:25.751451 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.09549 (* 0.0454545 = 0.140704 loss)
I0407 13:31:25.751466 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.16166 (* 0.0454545 = 0.0982572 loss)
I0407 13:31:25.751478 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.4619 (* 0.0454545 = 0.0664498 loss)
I0407 13:31:25.751492 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.732453 (* 0.0454545 = 0.0332933 loss)
I0407 13:31:25.751507 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.15194 (* 0.0454545 = 0.00690636 loss)
I0407 13:31:25.751520 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.175768 (* 0.0454545 = 0.00798947 loss)
I0407 13:31:25.751534 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 9.05886e-05 (* 0.0454545 = 4.11766e-06 loss)
I0407 13:31:25.751549 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 8.84033e-05 (* 0.0454545 = 4.01833e-06 loss)
I0407 13:31:25.751564 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 9.92619e-05 (* 0.0454545 = 4.51191e-06 loss)
I0407 13:31:25.751577 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 9.67049e-05 (* 0.0454545 = 4.39568e-06 loss)
I0407 13:31:25.751591 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.97055e-05 (* 0.0454545 = 3.62298e-06 loss)
I0407 13:31:25.751605 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 9.851e-05 (* 0.0454545 = 4.47773e-06 loss)
I0407 13:31:25.751619 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 9.72587e-05 (* 0.0454545 = 4.42085e-06 loss)
I0407 13:31:25.751652 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 9.34522e-05 (* 0.0454545 = 4.24783e-06 loss)
I0407 13:31:25.751667 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 9.66275e-05 (* 0.0454545 = 4.39216e-06 loss)
I0407 13:31:25.751682 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000102842 (* 0.0454545 = 4.67462e-06 loss)
I0407 13:31:25.751695 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000105184 (* 0.0454545 = 4.7811e-06 loss)
I0407 13:31:25.751709 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 8.9013e-05 (* 0.0454545 = 4.04604e-06 loss)
I0407 13:31:25.751721 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:31:25.751732 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000467223
I0407 13:31:25.751747 32304 sgd_solver.cpp:106] Iteration 28500, lr = 0.00943
I0407 13:32:37.975294 32304 solver.cpp:229] Iteration 29000, loss = 0.954226
I0407 13:32:37.975431 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.15625
I0407 13:32:37.975450 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:32:37.975466 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 13:32:37.975478 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 13:32:37.975491 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 13:32:37.975502 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.28125
I0407 13:32:37.975513 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 13:32:37.975525 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:32:37.975538 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:32:37.975548 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:32:37.975560 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:32:37.975571 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:32:37.975584 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:32:37.975594 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:32:37.975606 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:32:37.975618 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:32:37.975630 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:32:37.975641 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:32:37.975653 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:32:37.975664 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:32:37.975677 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:32:37.975687 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:32:37.975703 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.90367 (* 0.0454545 = 0.131985 loss)
I0407 13:32:37.975718 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.44356 (* 0.0454545 = 0.156525 loss)
I0407 13:32:37.975731 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.61474 (* 0.0454545 = 0.164306 loss)
I0407 13:32:37.975745 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.45862 (* 0.0454545 = 0.15721 loss)
I0407 13:32:37.975759 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.34376 (* 0.0454545 = 0.151989 loss)
I0407 13:32:37.975774 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.96795 (* 0.0454545 = 0.134907 loss)
I0407 13:32:37.975787 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.619 (* 0.0454545 = 0.0735909 loss)
I0407 13:32:37.975801 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.451256 (* 0.0454545 = 0.0205116 loss)
I0407 13:32:37.975816 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0549465 (* 0.0454545 = 0.00249757 loss)
I0407 13:32:37.975829 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0148268 (* 0.0454545 = 0.000673946 loss)
I0407 13:32:37.975843 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.04778e-05 (* 0.0454545 = 1.8399e-06 loss)
I0407 13:32:37.975858 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.33035e-05 (* 0.0454545 = 1.96834e-06 loss)
I0407 13:32:37.975872 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.30156e-05 (* 0.0454545 = 1.95525e-06 loss)
I0407 13:32:37.975885 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.209e-05 (* 0.0454545 = 1.91318e-06 loss)
I0407 13:32:37.975899 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.03712e-05 (* 0.0454545 = 1.83506e-06 loss)
I0407 13:32:37.975914 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.19035e-05 (* 0.0454545 = 1.9047e-06 loss)
I0407 13:32:37.975927 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.22103e-05 (* 0.0454545 = 1.91865e-06 loss)
I0407 13:32:37.975958 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.29851e-05 (* 0.0454545 = 1.95387e-06 loss)
I0407 13:32:37.975973 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.23462e-05 (* 0.0454545 = 1.92483e-06 loss)
I0407 13:32:37.975987 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.38556e-05 (* 0.0454545 = 1.99344e-06 loss)
I0407 13:32:37.976001 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.47619e-05 (* 0.0454545 = 2.03463e-06 loss)
I0407 13:32:37.976016 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.03617e-05 (* 0.0454545 = 1.83462e-06 loss)
I0407 13:32:37.976027 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:32:37.976038 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.0001657
I0407 13:32:37.976053 32304 sgd_solver.cpp:106] Iteration 29000, lr = 0.00942
I0407 13:33:50.265240 32304 solver.cpp:229] Iteration 29500, loss = 0.94894
I0407 13:33:50.265360 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:33:50.265380 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:33:50.265393 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:33:50.265405 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:33:50.265418 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 13:33:50.265429 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.46875
I0407 13:33:50.265440 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 13:33:50.265452 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.78125
I0407 13:33:50.265463 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:33:50.265475 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:33:50.265486 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:33:50.265498 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:33:50.265509 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:33:50.265522 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:33:50.265532 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:33:50.265543 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:33:50.265554 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:33:50.265565 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:33:50.265576 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:33:50.265588 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:33:50.265599 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:33:50.265610 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:33:50.265626 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.9629 (* 0.0454545 = 0.134677 loss)
I0407 13:33:50.265640 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.15032 (* 0.0454545 = 0.143196 loss)
I0407 13:33:50.265655 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.18963 (* 0.0454545 = 0.144983 loss)
I0407 13:33:50.265667 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.18837 (* 0.0454545 = 0.144926 loss)
I0407 13:33:50.265681 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.69102 (* 0.0454545 = 0.122319 loss)
I0407 13:33:50.265696 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.29939 (* 0.0454545 = 0.104518 loss)
I0407 13:33:50.265709 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.56213 (* 0.0454545 = 0.0710061 loss)
I0407 13:33:50.265723 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.11844 (* 0.0454545 = 0.0508383 loss)
I0407 13:33:50.265736 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0420853 (* 0.0454545 = 0.00191297 loss)
I0407 13:33:50.265751 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0106255 (* 0.0454545 = 0.000482976 loss)
I0407 13:33:50.265765 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000105946 (* 0.0454545 = 4.81575e-06 loss)
I0407 13:33:50.265784 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000109617 (* 0.0454545 = 4.98261e-06 loss)
I0407 13:33:50.265799 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000106241 (* 0.0454545 = 4.82912e-06 loss)
I0407 13:33:50.265812 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000104392 (* 0.0454545 = 4.74511e-06 loss)
I0407 13:33:50.265826 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000107461 (* 0.0454545 = 4.88459e-06 loss)
I0407 13:33:50.265841 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.00010966 (* 0.0454545 = 4.98456e-06 loss)
I0407 13:33:50.265854 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000104226 (* 0.0454545 = 4.73756e-06 loss)
I0407 13:33:50.265884 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000106322 (* 0.0454545 = 4.83284e-06 loss)
I0407 13:33:50.265899 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000104541 (* 0.0454545 = 4.75186e-06 loss)
I0407 13:33:50.265913 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000112816 (* 0.0454545 = 5.12798e-06 loss)
I0407 13:33:50.265928 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000110799 (* 0.0454545 = 5.03633e-06 loss)
I0407 13:33:50.265941 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000106281 (* 0.0454545 = 4.83095e-06 loss)
I0407 13:33:50.265952 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:33:50.265964 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000160591
I0407 13:33:50.265980 32304 sgd_solver.cpp:106] Iteration 29500, lr = 0.00941
I0407 13:35:02.240625 32304 solver.cpp:338] Iteration 30000, Testing net (#0)
I0407 13:35:10.318712 32304 solver.cpp:393] Test loss: 0.867534
I0407 13:35:10.318781 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.132
I0407 13:35:10.318797 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.06
I0407 13:35:10.318810 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.08
I0407 13:35:10.318821 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.119
I0407 13:35:10.318833 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.213
I0407 13:35:10.318852 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.498
I0407 13:35:10.318864 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.893
I0407 13:35:10.318876 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 13:35:10.318887 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 13:35:10.318898 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 13:35:10.318910 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 13:35:10.318924 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 13:35:10.318936 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 13:35:10.318948 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 13:35:10.318958 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 13:35:10.318969 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 13:35:10.318980 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 13:35:10.318992 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 13:35:10.319003 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 13:35:10.319015 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 13:35:10.319025 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 13:35:10.319036 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 13:35:10.319052 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 3.10093 (* 0.0454545 = 0.140951 loss)
I0407 13:35:10.319067 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.24237 (* 0.0454545 = 0.147381 loss)
I0407 13:35:10.319080 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 3.2709 (* 0.0454545 = 0.148677 loss)
I0407 13:35:10.319093 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 3.19948 (* 0.0454545 = 0.145431 loss)
I0407 13:35:10.319106 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 3.01777 (* 0.0454545 = 0.137171 loss)
I0407 13:35:10.319120 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 2.15525 (* 0.0454545 = 0.0979661 loss)
I0407 13:35:10.319133 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.767671 (* 0.0454545 = 0.0348941 loss)
I0407 13:35:10.319146 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.248451 (* 0.0454545 = 0.0112932 loss)
I0407 13:35:10.319160 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0553376 (* 0.0454545 = 0.00251535 loss)
I0407 13:35:10.319175 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0260548 (* 0.0454545 = 0.00118431 loss)
I0407 13:35:10.319188 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 0.000123575 (* 0.0454545 = 5.61705e-06 loss)
I0407 13:35:10.319202 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 0.000141707 (* 0.0454545 = 6.44125e-06 loss)
I0407 13:35:10.319216 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 0.000129401 (* 0.0454545 = 5.88185e-06 loss)
I0407 13:35:10.319231 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 0.000120391 (* 0.0454545 = 5.47231e-06 loss)
I0407 13:35:10.319244 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 0.000122087 (* 0.0454545 = 5.5494e-06 loss)
I0407 13:35:10.319257 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 0.00012118 (* 0.0454545 = 5.50817e-06 loss)
I0407 13:35:10.319272 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 0.000132018 (* 0.0454545 = 6.00081e-06 loss)
I0407 13:35:10.319341 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 0.000129011 (* 0.0454545 = 5.86416e-06 loss)
I0407 13:35:10.319358 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 0.000139865 (* 0.0454545 = 6.3575e-06 loss)
I0407 13:35:10.319372 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 0.000125648 (* 0.0454545 = 5.71128e-06 loss)
I0407 13:35:10.319386 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 0.000130777 (* 0.0454545 = 5.94441e-06 loss)
I0407 13:35:10.319399 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 0.000133542 (* 0.0454545 = 6.07009e-06 loss)
I0407 13:35:10.319411 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0
I0407 13:35:10.319423 32304 solver.cpp:406]     Test net output #45: total_confidence = 0.000156932
I0407 13:35:10.353775 32304 solver.cpp:229] Iteration 30000, loss = 0.948149
I0407 13:35:10.353834 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 13:35:10.353852 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:35:10.353864 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:35:10.353876 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:35:10.353888 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 13:35:10.353900 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 13:35:10.353912 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 13:35:10.353924 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 13:35:10.353936 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:35:10.353947 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:35:10.353960 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:35:10.353971 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:35:10.353982 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:35:10.353994 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:35:10.354006 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:35:10.354017 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:35:10.354028 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:35:10.354040 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:35:10.354051 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:35:10.354063 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:35:10.354076 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:35:10.354089 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:35:10.354104 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.74876 (* 0.0454545 = 0.124944 loss)
I0407 13:35:10.354120 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.19796 (* 0.0454545 = 0.145362 loss)
I0407 13:35:10.354132 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.13872 (* 0.0454545 = 0.142669 loss)
I0407 13:35:10.354146 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.19208 (* 0.0454545 = 0.145095 loss)
I0407 13:35:10.354161 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.71375 (* 0.0454545 = 0.123352 loss)
I0407 13:35:10.354174 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.19357 (* 0.0454545 = 0.0997076 loss)
I0407 13:35:10.354188 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.80544 (* 0.0454545 = 0.0820654 loss)
I0407 13:35:10.354202 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.505936 (* 0.0454545 = 0.0229971 loss)
I0407 13:35:10.354215 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.351246 (* 0.0454545 = 0.0159657 loss)
I0407 13:35:10.354229 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0168943 (* 0.0454545 = 0.000767922 loss)
I0407 13:35:10.354269 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 7.13938e-05 (* 0.0454545 = 3.24517e-06 loss)
I0407 13:35:10.354284 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 8.16598e-05 (* 0.0454545 = 3.71181e-06 loss)
I0407 13:35:10.354302 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.30982e-05 (* 0.0454545 = 3.32265e-06 loss)
I0407 13:35:10.354317 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.68132e-05 (* 0.0454545 = 3.03696e-06 loss)
I0407 13:35:10.354331 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.15674e-05 (* 0.0454545 = 3.25306e-06 loss)
I0407 13:35:10.354346 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.9024e-05 (* 0.0454545 = 3.13746e-06 loss)
I0407 13:35:10.354359 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 7.66909e-05 (* 0.0454545 = 3.48595e-06 loss)
I0407 13:35:10.354373 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.56857e-05 (* 0.0454545 = 3.44026e-06 loss)
I0407 13:35:10.354387 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.95538e-05 (* 0.0454545 = 3.61608e-06 loss)
I0407 13:35:10.354401 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.48043e-05 (* 0.0454545 = 3.4002e-06 loss)
I0407 13:35:10.354415 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 7.30519e-05 (* 0.0454545 = 3.32054e-06 loss)
I0407 13:35:10.354429 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.66277e-05 (* 0.0454545 = 3.48308e-06 loss)
I0407 13:35:10.354441 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:35:10.354452 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000448959
I0407 13:35:10.354467 32304 sgd_solver.cpp:106] Iteration 30000, lr = 0.0094
I0407 13:36:22.040558 32304 solver.cpp:229] Iteration 30500, loss = 0.949707
I0407 13:36:22.040712 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 13:36:22.040732 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.15625
I0407 13:36:22.040745 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:36:22.040757 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:36:22.040769 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.3125
I0407 13:36:22.040781 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 13:36:22.040793 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 13:36:22.040804 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 13:36:22.040817 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:36:22.040828 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:36:22.040840 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:36:22.040851 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:36:22.040863 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:36:22.040874 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:36:22.040886 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:36:22.040899 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:36:22.040910 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:36:22.040925 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:36:22.040935 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:36:22.040947 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:36:22.040958 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:36:22.040971 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:36:22.040985 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.06023 (* 0.0454545 = 0.139101 loss)
I0407 13:36:22.041000 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.18639 (* 0.0454545 = 0.144836 loss)
I0407 13:36:22.041013 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.38229 (* 0.0454545 = 0.15374 loss)
I0407 13:36:22.041028 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.12957 (* 0.0454545 = 0.142253 loss)
I0407 13:36:22.041041 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.75365 (* 0.0454545 = 0.125166 loss)
I0407 13:36:22.041055 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.17413 (* 0.0454545 = 0.0988243 loss)
I0407 13:36:22.041069 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.13862 (* 0.0454545 = 0.0517555 loss)
I0407 13:36:22.041082 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.568721 (* 0.0454545 = 0.0258509 loss)
I0407 13:36:22.041097 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.190513 (* 0.0454545 = 0.00865969 loss)
I0407 13:36:22.041111 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0151867 (* 0.0454545 = 0.000690306 loss)
I0407 13:36:22.041126 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 1.22117e-05 (* 0.0454545 = 5.55079e-07 loss)
I0407 13:36:22.041139 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 1.2968e-05 (* 0.0454545 = 5.89454e-07 loss)
I0407 13:36:22.041153 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 1.12171e-05 (* 0.0454545 = 5.09866e-07 loss)
I0407 13:36:22.041167 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 1.05241e-05 (* 0.0454545 = 4.78369e-07 loss)
I0407 13:36:22.041182 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 1.18131e-05 (* 0.0454545 = 5.36959e-07 loss)
I0407 13:36:22.041194 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 1.13474e-05 (* 0.0454545 = 5.15793e-07 loss)
I0407 13:36:22.041209 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 1.25284e-05 (* 0.0454545 = 5.69473e-07 loss)
I0407 13:36:22.041240 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 1.24986e-05 (* 0.0454545 = 5.68117e-07 loss)
I0407 13:36:22.041255 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 1.27669e-05 (* 0.0454545 = 5.80312e-07 loss)
I0407 13:36:22.041270 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 1.17945e-05 (* 0.0454545 = 5.36113e-07 loss)
I0407 13:36:22.041283 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 1.14741e-05 (* 0.0454545 = 5.21551e-07 loss)
I0407 13:36:22.041297 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 1.2331e-05 (* 0.0454545 = 5.60498e-07 loss)
I0407 13:36:22.041309 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:36:22.041321 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000331867
I0407 13:36:22.041337 32304 sgd_solver.cpp:106] Iteration 30500, lr = 0.00939
I0407 13:37:34.167735 32304 solver.cpp:229] Iteration 31000, loss = 0.947322
I0407 13:37:34.167868 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:37:34.167887 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:37:34.167901 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 13:37:34.167913 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 13:37:34.167928 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 13:37:34.167940 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.25
I0407 13:37:34.167953 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 13:37:34.167964 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:37:34.167975 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:37:34.167987 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:37:34.167999 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:37:34.168010 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:37:34.168022 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:37:34.168033 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:37:34.168045 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:37:34.168056 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:37:34.168067 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:37:34.168079 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:37:34.168090 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:37:34.168102 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:37:34.168113 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:37:34.168124 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:37:34.168141 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.05824 (* 0.0454545 = 0.139011 loss)
I0407 13:37:34.168155 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.40469 (* 0.0454545 = 0.154759 loss)
I0407 13:37:34.168169 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.40172 (* 0.0454545 = 0.154623 loss)
I0407 13:37:34.168184 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.14483 (* 0.0454545 = 0.142947 loss)
I0407 13:37:34.168196 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.96722 (* 0.0454545 = 0.134874 loss)
I0407 13:37:34.168210 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.98045 (* 0.0454545 = 0.135475 loss)
I0407 13:37:34.168223 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.43265 (* 0.0454545 = 0.0651206 loss)
I0407 13:37:34.168237 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.460628 (* 0.0454545 = 0.0209376 loss)
I0407 13:37:34.168251 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.23873 (* 0.0454545 = 0.0108514 loss)
I0407 13:37:34.168265 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.198315 (* 0.0454545 = 0.00901431 loss)
I0407 13:37:34.168279 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000288871 (* 0.0454545 = 1.31305e-05 loss)
I0407 13:37:34.168293 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000311822 (* 0.0454545 = 1.41737e-05 loss)
I0407 13:37:34.168306 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000341756 (* 0.0454545 = 1.55344e-05 loss)
I0407 13:37:34.168320 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000317135 (* 0.0454545 = 1.44152e-05 loss)
I0407 13:37:34.168334 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000264854 (* 0.0454545 = 1.20388e-05 loss)
I0407 13:37:34.168349 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000332465 (* 0.0454545 = 1.5112e-05 loss)
I0407 13:37:34.168362 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000345696 (* 0.0454545 = 1.57135e-05 loss)
I0407 13:37:34.168392 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000314836 (* 0.0454545 = 1.43107e-05 loss)
I0407 13:37:34.168408 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000306556 (* 0.0454545 = 1.39344e-05 loss)
I0407 13:37:34.168421 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000325359 (* 0.0454545 = 1.4789e-05 loss)
I0407 13:37:34.168436 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000344832 (* 0.0454545 = 1.56742e-05 loss)
I0407 13:37:34.168449 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000286873 (* 0.0454545 = 1.30397e-05 loss)
I0407 13:37:34.168462 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:37:34.168473 32304 solver.cpp:245]     Train net output #45: total_confidence = 5.70818e-05
I0407 13:37:34.168488 32304 sgd_solver.cpp:106] Iteration 31000, lr = 0.00938
I0407 13:38:46.081976 32304 solver.cpp:229] Iteration 31500, loss = 0.942455
I0407 13:38:46.082151 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:38:46.082182 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 13:38:46.082201 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:38:46.082213 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:38:46.082224 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.34375
I0407 13:38:46.082237 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5625
I0407 13:38:46.082248 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:38:46.082260 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:38:46.082273 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:38:46.082283 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:38:46.082295 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:38:46.082307 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:38:46.082319 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:38:46.082330 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:38:46.082341 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:38:46.082353 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:38:46.082365 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:38:46.082376 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:38:46.082387 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:38:46.082398 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:38:46.082409 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:38:46.082420 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:38:46.082437 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.0562 (* 0.0454545 = 0.138918 loss)
I0407 13:38:46.082450 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.4583 (* 0.0454545 = 0.157195 loss)
I0407 13:38:46.082464 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.71364 (* 0.0454545 = 0.168802 loss)
I0407 13:38:46.082479 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.67581 (* 0.0454545 = 0.167082 loss)
I0407 13:38:46.082491 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.77229 (* 0.0454545 = 0.126013 loss)
I0407 13:38:46.082505 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.09534 (* 0.0454545 = 0.0952427 loss)
I0407 13:38:46.082520 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.38974 (* 0.0454545 = 0.06317 loss)
I0407 13:38:46.082532 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.492872 (* 0.0454545 = 0.0224033 loss)
I0407 13:38:46.082547 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.211805 (* 0.0454545 = 0.00962748 loss)
I0407 13:38:46.082561 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00413233 (* 0.0454545 = 0.000187833 loss)
I0407 13:38:46.082576 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 3.65923e-05 (* 0.0454545 = 1.66329e-06 loss)
I0407 13:38:46.082589 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 3.7479e-05 (* 0.0454545 = 1.70359e-06 loss)
I0407 13:38:46.082603 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 3.49529e-05 (* 0.0454545 = 1.58877e-06 loss)
I0407 13:38:46.082617 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 3.54148e-05 (* 0.0454545 = 1.60977e-06 loss)
I0407 13:38:46.082631 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.46212e-05 (* 0.0454545 = 1.57369e-06 loss)
I0407 13:38:46.082645 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.87443e-05 (* 0.0454545 = 1.76111e-06 loss)
I0407 13:38:46.082659 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 3.79858e-05 (* 0.0454545 = 1.72663e-06 loss)
I0407 13:38:46.082690 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.01486e-05 (* 0.0454545 = 1.82494e-06 loss)
I0407 13:38:46.082706 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 3.68864e-05 (* 0.0454545 = 1.67666e-06 loss)
I0407 13:38:46.082720 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 3.73115e-05 (* 0.0454545 = 1.69598e-06 loss)
I0407 13:38:46.082733 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 3.64452e-05 (* 0.0454545 = 1.6566e-06 loss)
I0407 13:38:46.082748 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.55971e-05 (* 0.0454545 = 1.61805e-06 loss)
I0407 13:38:46.082761 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:38:46.082772 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000632982
I0407 13:38:46.082787 32304 sgd_solver.cpp:106] Iteration 31500, lr = 0.00937
I0407 13:39:58.203217 32304 solver.cpp:229] Iteration 32000, loss = 0.938183
I0407 13:39:58.203358 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:39:58.203379 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 13:39:58.203392 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:39:58.203404 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:39:58.203418 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 13:39:58.203428 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 13:39:58.203440 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 13:39:58.203451 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:39:58.203464 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:39:58.203475 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:39:58.203487 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:39:58.203498 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:39:58.203510 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:39:58.203521 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:39:58.203533 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:39:58.203544 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:39:58.203557 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:39:58.203567 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:39:58.203578 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:39:58.203590 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:39:58.203601 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:39:58.203613 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:39:58.203629 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.86191 (* 0.0454545 = 0.130087 loss)
I0407 13:39:58.203644 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.0184 (* 0.0454545 = 0.1372 loss)
I0407 13:39:58.203656 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.40848 (* 0.0454545 = 0.154931 loss)
I0407 13:39:58.203670 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.14904 (* 0.0454545 = 0.143138 loss)
I0407 13:39:58.203683 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.83182 (* 0.0454545 = 0.128719 loss)
I0407 13:39:58.203697 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.53765 (* 0.0454545 = 0.115348 loss)
I0407 13:39:58.203711 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.54608 (* 0.0454545 = 0.0702762 loss)
I0407 13:39:58.203724 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.511049 (* 0.0454545 = 0.0232295 loss)
I0407 13:39:58.203738 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.271568 (* 0.0454545 = 0.012344 loss)
I0407 13:39:58.203753 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0188422 (* 0.0454545 = 0.000856462 loss)
I0407 13:39:58.203768 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 3.98163e-05 (* 0.0454545 = 1.80983e-06 loss)
I0407 13:39:58.203781 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 3.71328e-05 (* 0.0454545 = 1.68785e-06 loss)
I0407 13:39:58.203795 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.22094e-05 (* 0.0454545 = 1.91861e-06 loss)
I0407 13:39:58.203809 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.11493e-05 (* 0.0454545 = 1.87042e-06 loss)
I0407 13:39:58.203824 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.39733e-05 (* 0.0454545 = 1.54424e-06 loss)
I0407 13:39:58.203836 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.88218e-05 (* 0.0454545 = 1.76463e-06 loss)
I0407 13:39:58.203850 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.10333e-05 (* 0.0454545 = 1.86515e-06 loss)
I0407 13:39:58.203883 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 3.66931e-05 (* 0.0454545 = 1.66787e-06 loss)
I0407 13:39:58.203898 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.11636e-05 (* 0.0454545 = 1.87107e-06 loss)
I0407 13:39:58.203912 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.07946e-05 (* 0.0454545 = 1.8543e-06 loss)
I0407 13:39:58.203928 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.05085e-05 (* 0.0454545 = 1.8413e-06 loss)
I0407 13:39:58.203943 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.82118e-05 (* 0.0454545 = 1.7369e-06 loss)
I0407 13:39:58.203954 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:39:58.203966 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000680656
I0407 13:39:58.203981 32304 sgd_solver.cpp:106] Iteration 32000, lr = 0.00936
I0407 13:41:10.822811 32304 solver.cpp:229] Iteration 32500, loss = 0.93924
I0407 13:41:10.822958 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 13:41:10.822978 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 13:41:10.822990 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 13:41:10.823004 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.1875
I0407 13:41:10.823015 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 13:41:10.823027 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 13:41:10.823038 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:41:10.823050 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 13:41:10.823062 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:41:10.823073 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:41:10.823086 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:41:10.823096 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:41:10.823108 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:41:10.823119 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:41:10.823132 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:41:10.823143 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:41:10.823154 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:41:10.823165 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:41:10.823176 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:41:10.823187 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:41:10.823199 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:41:10.823210 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:41:10.823226 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.06403 (* 0.0454545 = 0.139274 loss)
I0407 13:41:10.823240 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.32171 (* 0.0454545 = 0.150987 loss)
I0407 13:41:10.823254 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.42242 (* 0.0454545 = 0.155564 loss)
I0407 13:41:10.823267 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.23221 (* 0.0454545 = 0.146919 loss)
I0407 13:41:10.823282 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.03826 (* 0.0454545 = 0.138103 loss)
I0407 13:41:10.823295 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.75426 (* 0.0454545 = 0.125194 loss)
I0407 13:41:10.823309 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.08334 (* 0.0454545 = 0.0492426 loss)
I0407 13:41:10.823339 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.566801 (* 0.0454545 = 0.0257637 loss)
I0407 13:41:10.823355 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.269307 (* 0.0454545 = 0.0122412 loss)
I0407 13:41:10.823370 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.167831 (* 0.0454545 = 0.00762867 loss)
I0407 13:41:10.823385 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 5.87605e-05 (* 0.0454545 = 2.67093e-06 loss)
I0407 13:41:10.823400 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 6.12268e-05 (* 0.0454545 = 2.78304e-06 loss)
I0407 13:41:10.823413 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.53377e-05 (* 0.0454545 = 2.51535e-06 loss)
I0407 13:41:10.823427 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 5.54386e-05 (* 0.0454545 = 2.51994e-06 loss)
I0407 13:41:10.823441 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 5.70057e-05 (* 0.0454545 = 2.59117e-06 loss)
I0407 13:41:10.823456 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 5.44774e-05 (* 0.0454545 = 2.47625e-06 loss)
I0407 13:41:10.823468 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 5.95128e-05 (* 0.0454545 = 2.70513e-06 loss)
I0407 13:41:10.823503 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 6.23266e-05 (* 0.0454545 = 2.83303e-06 loss)
I0407 13:41:10.823518 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.36656e-05 (* 0.0454545 = 2.89389e-06 loss)
I0407 13:41:10.823531 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 5.5761e-05 (* 0.0454545 = 2.53459e-06 loss)
I0407 13:41:10.823545 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 5.48927e-05 (* 0.0454545 = 2.49512e-06 loss)
I0407 13:41:10.823559 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.1978e-05 (* 0.0454545 = 2.81718e-06 loss)
I0407 13:41:10.823570 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:41:10.823582 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000522443
I0407 13:41:10.823598 32304 sgd_solver.cpp:106] Iteration 32500, lr = 0.00935
I0407 13:42:23.465224 32304 solver.cpp:229] Iteration 33000, loss = 0.934448
I0407 13:42:23.465373 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:42:23.465392 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:42:23.465406 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:42:23.465418 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:42:23.465430 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 13:42:23.465442 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 13:42:23.465454 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 13:42:23.465466 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 13:42:23.465478 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:42:23.465489 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:42:23.465502 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:42:23.465512 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:42:23.465523 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:42:23.465535 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:42:23.465548 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:42:23.465559 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:42:23.465569 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:42:23.465581 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:42:23.465592 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:42:23.465603 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:42:23.465615 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:42:23.465626 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:42:23.465641 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.89411 (* 0.0454545 = 0.13155 loss)
I0407 13:42:23.465656 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.30758 (* 0.0454545 = 0.150345 loss)
I0407 13:42:23.465670 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.16173 (* 0.0454545 = 0.143715 loss)
I0407 13:42:23.465683 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.02122 (* 0.0454545 = 0.137328 loss)
I0407 13:42:23.465698 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.03122 (* 0.0454545 = 0.137783 loss)
I0407 13:42:23.465710 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.4293 (* 0.0454545 = 0.110423 loss)
I0407 13:42:23.465724 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.05216 (* 0.0454545 = 0.0478253 loss)
I0407 13:42:23.465739 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.214823 (* 0.0454545 = 0.00976469 loss)
I0407 13:42:23.465752 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0463909 (* 0.0454545 = 0.00210868 loss)
I0407 13:42:23.465766 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0181804 (* 0.0454545 = 0.000826383 loss)
I0407 13:42:23.465780 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.0002236 (* 0.0454545 = 1.01636e-05 loss)
I0407 13:42:23.465795 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000228196 (* 0.0454545 = 1.03725e-05 loss)
I0407 13:42:23.465809 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000238022 (* 0.0454545 = 1.08192e-05 loss)
I0407 13:42:23.465823 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00024341 (* 0.0454545 = 1.10641e-05 loss)
I0407 13:42:23.465837 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000207538 (* 0.0454545 = 9.43355e-06 loss)
I0407 13:42:23.465850 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000232784 (* 0.0454545 = 1.05811e-05 loss)
I0407 13:42:23.465864 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000262369 (* 0.0454545 = 1.19259e-05 loss)
I0407 13:42:23.465895 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000226473 (* 0.0454545 = 1.02942e-05 loss)
I0407 13:42:23.465910 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.0002526 (* 0.0454545 = 1.14818e-05 loss)
I0407 13:42:23.465929 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000241935 (* 0.0454545 = 1.0997e-05 loss)
I0407 13:42:23.465942 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000237136 (* 0.0454545 = 1.07789e-05 loss)
I0407 13:42:23.465956 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000227269 (* 0.0454545 = 1.03304e-05 loss)
I0407 13:42:23.465967 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:42:23.465980 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000134776
I0407 13:42:23.465994 32304 sgd_solver.cpp:106] Iteration 33000, lr = 0.00934
I0407 13:43:36.270655 32304 solver.cpp:229] Iteration 33500, loss = 0.929889
I0407 13:43:36.270854 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:43:36.270875 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:43:36.270889 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:43:36.270910 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:43:36.270925 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 13:43:36.270937 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 13:43:36.270949 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 13:43:36.270961 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:43:36.270973 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:43:36.270992 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:43:36.271003 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:43:36.271014 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:43:36.271025 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:43:36.271036 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:43:36.271056 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:43:36.271069 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:43:36.271080 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:43:36.271090 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:43:36.271102 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:43:36.271113 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:43:36.271124 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:43:36.271136 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:43:36.271152 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.16088 (* 0.0454545 = 0.143676 loss)
I0407 13:43:36.271167 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.11426 (* 0.0454545 = 0.141557 loss)
I0407 13:43:36.271180 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.37813 (* 0.0454545 = 0.153552 loss)
I0407 13:43:36.271193 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.15693 (* 0.0454545 = 0.143497 loss)
I0407 13:43:36.271208 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.82886 (* 0.0454545 = 0.128585 loss)
I0407 13:43:36.271221 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.43617 (* 0.0454545 = 0.110735 loss)
I0407 13:43:36.271234 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.80095 (* 0.0454545 = 0.0818613 loss)
I0407 13:43:36.271248 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.530511 (* 0.0454545 = 0.0241141 loss)
I0407 13:43:36.271262 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.373328 (* 0.0454545 = 0.0169694 loss)
I0407 13:43:36.271276 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0118293 (* 0.0454545 = 0.000537695 loss)
I0407 13:43:36.271291 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 7.28313e-05 (* 0.0454545 = 3.31051e-06 loss)
I0407 13:43:36.271306 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.62197e-05 (* 0.0454545 = 3.46453e-06 loss)
I0407 13:43:36.271339 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 6.64366e-05 (* 0.0454545 = 3.01985e-06 loss)
I0407 13:43:36.271364 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.28038e-05 (* 0.0454545 = 2.85472e-06 loss)
I0407 13:43:36.271378 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 6.80333e-05 (* 0.0454545 = 3.09242e-06 loss)
I0407 13:43:36.271410 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.35794e-05 (* 0.0454545 = 2.88997e-06 loss)
I0407 13:43:36.271426 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 7.20127e-05 (* 0.0454545 = 3.2733e-06 loss)
I0407 13:43:36.271457 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.17297e-05 (* 0.0454545 = 3.26044e-06 loss)
I0407 13:43:36.271472 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.57893e-05 (* 0.0454545 = 3.44497e-06 loss)
I0407 13:43:36.271486 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 6.60289e-05 (* 0.0454545 = 3.00131e-06 loss)
I0407 13:43:36.271505 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.31388e-05 (* 0.0454545 = 2.86995e-06 loss)
I0407 13:43:36.271519 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.28362e-05 (* 0.0454545 = 3.31074e-06 loss)
I0407 13:43:36.271531 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:43:36.271544 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000331504
I0407 13:43:36.271559 32304 sgd_solver.cpp:106] Iteration 33500, lr = 0.00933
I0407 13:44:48.804777 32304 solver.cpp:229] Iteration 34000, loss = 0.931419
I0407 13:44:48.804903 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:44:48.804925 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 13:44:48.804939 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.15625
I0407 13:44:48.804951 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 13:44:48.804963 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 13:44:48.804975 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 13:44:48.804986 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 13:44:48.804997 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 13:44:48.805009 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:44:48.805022 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:44:48.805032 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:44:48.805043 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:44:48.805054 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:44:48.805066 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:44:48.805078 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:44:48.805088 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:44:48.805099 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:44:48.805111 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:44:48.805122 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:44:48.805133 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:44:48.805145 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:44:48.805156 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:44:48.805172 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.72385 (* 0.0454545 = 0.123811 loss)
I0407 13:44:48.805193 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.0542 (* 0.0454545 = 0.138827 loss)
I0407 13:44:48.805220 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.28632 (* 0.0454545 = 0.149378 loss)
I0407 13:44:48.805238 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.98243 (* 0.0454545 = 0.135565 loss)
I0407 13:44:48.805251 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.92764 (* 0.0454545 = 0.133075 loss)
I0407 13:44:48.805264 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.374 (* 0.0454545 = 0.107909 loss)
I0407 13:44:48.805284 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.43073 (* 0.0454545 = 0.0650331 loss)
I0407 13:44:48.805311 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.555632 (* 0.0454545 = 0.025256 loss)
I0407 13:44:48.805330 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0476405 (* 0.0454545 = 0.00216548 loss)
I0407 13:44:48.805344 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0162039 (* 0.0454545 = 0.000736542 loss)
I0407 13:44:48.805358 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.4842e-05 (* 0.0454545 = 3.85645e-06 loss)
I0407 13:44:48.805372 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 8.98867e-05 (* 0.0454545 = 4.08576e-06 loss)
I0407 13:44:48.805387 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 8.42342e-05 (* 0.0454545 = 3.82883e-06 loss)
I0407 13:44:48.805400 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 7.986e-05 (* 0.0454545 = 3.63e-06 loss)
I0407 13:44:48.805414 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 8.85458e-05 (* 0.0454545 = 4.02481e-06 loss)
I0407 13:44:48.805428 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 8.3578e-05 (* 0.0454545 = 3.799e-06 loss)
I0407 13:44:48.805443 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 8.54802e-05 (* 0.0454545 = 3.88547e-06 loss)
I0407 13:44:48.805474 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 8.81695e-05 (* 0.0454545 = 4.00771e-06 loss)
I0407 13:44:48.805490 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 8.64674e-05 (* 0.0454545 = 3.93034e-06 loss)
I0407 13:44:48.805502 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 8.34312e-05 (* 0.0454545 = 3.79233e-06 loss)
I0407 13:44:48.805516 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 8.0082e-05 (* 0.0454545 = 3.64009e-06 loss)
I0407 13:44:48.805531 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 8.59054e-05 (* 0.0454545 = 3.90479e-06 loss)
I0407 13:44:48.805542 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:44:48.805554 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000204873
I0407 13:44:48.805568 32304 sgd_solver.cpp:106] Iteration 34000, lr = 0.00932
I0407 13:46:01.347008 32304 solver.cpp:229] Iteration 34500, loss = 0.929962
I0407 13:46:01.347131 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 13:46:01.347152 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 13:46:01.347165 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 13:46:01.347177 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 13:46:01.347189 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 13:46:01.347201 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.25
I0407 13:46:01.347213 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 13:46:01.347225 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 13:46:01.347237 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:46:01.347249 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:46:01.347260 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:46:01.347271 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:46:01.347283 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:46:01.347295 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:46:01.347306 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:46:01.347333 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:46:01.347349 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:46:01.347360 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:46:01.347373 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:46:01.347383 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:46:01.347395 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:46:01.347406 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:46:01.347422 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.3425 (* 0.0454545 = 0.151932 loss)
I0407 13:46:01.347437 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.1382 (* 0.0454545 = 0.142645 loss)
I0407 13:46:01.347451 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.27198 (* 0.0454545 = 0.148726 loss)
I0407 13:46:01.347465 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.50078 (* 0.0454545 = 0.159126 loss)
I0407 13:46:01.347478 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.46812 (* 0.0454545 = 0.157642 loss)
I0407 13:46:01.347491 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.98119 (* 0.0454545 = 0.135509 loss)
I0407 13:46:01.347506 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.09983 (* 0.0454545 = 0.0499921 loss)
I0407 13:46:01.347519 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.2688 (* 0.0454545 = 0.0122182 loss)
I0407 13:46:01.347534 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.03065 (* 0.0454545 = 0.00139318 loss)
I0407 13:46:01.347548 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0125314 (* 0.0454545 = 0.00056961 loss)
I0407 13:46:01.347563 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.68857e-05 (* 0.0454545 = 2.13117e-06 loss)
I0407 13:46:01.347578 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 5.05856e-05 (* 0.0454545 = 2.29935e-06 loss)
I0407 13:46:01.347591 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.04449e-05 (* 0.0454545 = 2.29295e-06 loss)
I0407 13:46:01.347605 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.58552e-05 (* 0.0454545 = 2.08433e-06 loss)
I0407 13:46:01.347620 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.62495e-05 (* 0.0454545 = 2.10225e-06 loss)
I0407 13:46:01.347633 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.53316e-05 (* 0.0454545 = 2.06053e-06 loss)
I0407 13:46:01.347647 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 5.49209e-05 (* 0.0454545 = 2.4964e-06 loss)
I0407 13:46:01.347681 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.6027e-05 (* 0.0454545 = 2.09214e-06 loss)
I0407 13:46:01.347695 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 5.50447e-05 (* 0.0454545 = 2.50203e-06 loss)
I0407 13:46:01.347709 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.57699e-05 (* 0.0454545 = 2.08045e-06 loss)
I0407 13:46:01.347723 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.89805e-05 (* 0.0454545 = 2.22639e-06 loss)
I0407 13:46:01.347738 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 5.03075e-05 (* 0.0454545 = 2.2867e-06 loss)
I0407 13:46:01.347749 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:46:01.347761 32304 solver.cpp:245]     Train net output #45: total_confidence = 6.97724e-05
I0407 13:46:01.347775 32304 sgd_solver.cpp:106] Iteration 34500, lr = 0.00931
I0407 13:47:14.295279 32304 solver.cpp:338] Iteration 35000, Testing net (#0)
I0407 13:47:22.313153 32304 solver.cpp:393] Test loss: 0.877214
I0407 13:47:22.313208 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.167
I0407 13:47:22.313225 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.039
I0407 13:47:22.313237 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.082
I0407 13:47:22.313249 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.118
I0407 13:47:22.313261 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.214
I0407 13:47:22.313273 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.497
I0407 13:47:22.313284 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.894
I0407 13:47:22.313297 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 13:47:22.313308 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 13:47:22.313319 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 13:47:22.313330 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 13:47:22.313341 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 13:47:22.313354 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 13:47:22.313364 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 13:47:22.313375 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 13:47:22.313386 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 13:47:22.313397 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 13:47:22.313408 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 13:47:22.313419 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 13:47:22.313431 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 13:47:22.313441 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 13:47:22.313452 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 13:47:22.313467 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 3.222 (* 0.0454545 = 0.146455 loss)
I0407 13:47:22.313482 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.30097 (* 0.0454545 = 0.150044 loss)
I0407 13:47:22.313495 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 3.29835 (* 0.0454545 = 0.149925 loss)
I0407 13:47:22.313508 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 3.21299 (* 0.0454545 = 0.146045 loss)
I0407 13:47:22.313522 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 3.05126 (* 0.0454545 = 0.138694 loss)
I0407 13:47:22.313535 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 2.10717 (* 0.0454545 = 0.0957804 loss)
I0407 13:47:22.313549 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.758318 (* 0.0454545 = 0.034469 loss)
I0407 13:47:22.313562 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.257515 (* 0.0454545 = 0.0117052 loss)
I0407 13:47:22.313577 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0602569 (* 0.0454545 = 0.00273895 loss)
I0407 13:47:22.313591 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0277779 (* 0.0454545 = 0.00126263 loss)
I0407 13:47:22.313606 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 0.000179748 (* 0.0454545 = 8.17038e-06 loss)
I0407 13:47:22.313619 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 0.00018137 (* 0.0454545 = 8.24409e-06 loss)
I0407 13:47:22.313633 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 0.000172487 (* 0.0454545 = 7.84033e-06 loss)
I0407 13:47:22.313647 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 0.000165504 (* 0.0454545 = 7.5229e-06 loss)
I0407 13:47:22.313660 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 0.0001788 (* 0.0454545 = 8.12728e-06 loss)
I0407 13:47:22.313674 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 0.000169022 (* 0.0454545 = 7.68284e-06 loss)
I0407 13:47:22.313688 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 0.000182377 (* 0.0454545 = 8.28986e-06 loss)
I0407 13:47:22.313741 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 0.000177631 (* 0.0454545 = 8.07413e-06 loss)
I0407 13:47:22.313757 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 0.00018944 (* 0.0454545 = 8.61091e-06 loss)
I0407 13:47:22.313771 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 0.000169082 (* 0.0454545 = 7.68553e-06 loss)
I0407 13:47:22.313784 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 0.000162889 (* 0.0454545 = 7.40405e-06 loss)
I0407 13:47:22.313797 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 0.000177537 (* 0.0454545 = 8.06984e-06 loss)
I0407 13:47:22.313809 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0.001
I0407 13:47:22.313822 32304 solver.cpp:406]     Test net output #45: total_confidence = 0.000198732
I0407 13:47:22.348479 32304 solver.cpp:229] Iteration 35000, loss = 0.930399
I0407 13:47:22.348536 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:47:22.348553 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:47:22.348567 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:47:22.348578 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 13:47:22.348590 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 13:47:22.348603 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 13:47:22.348614 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:47:22.348625 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:47:22.348637 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:47:22.348649 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:47:22.348661 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:47:22.348673 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:47:22.348685 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:47:22.348696 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:47:22.348707 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:47:22.348719 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:47:22.348731 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:47:22.348742 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:47:22.348753 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:47:22.348764 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:47:22.348776 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:47:22.348788 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:47:22.348803 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.26429 (* 0.0454545 = 0.148377 loss)
I0407 13:47:22.348817 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.36548 (* 0.0454545 = 0.152976 loss)
I0407 13:47:22.348831 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.41788 (* 0.0454545 = 0.155358 loss)
I0407 13:47:22.348845 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.53175 (* 0.0454545 = 0.160534 loss)
I0407 13:47:22.348858 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.97183 (* 0.0454545 = 0.135083 loss)
I0407 13:47:22.348872 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.63807 (* 0.0454545 = 0.119912 loss)
I0407 13:47:22.348886 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.19446 (* 0.0454545 = 0.0542934 loss)
I0407 13:47:22.348899 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.564887 (* 0.0454545 = 0.0256767 loss)
I0407 13:47:22.348913 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.379675 (* 0.0454545 = 0.0172579 loss)
I0407 13:47:22.348951 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.259343 (* 0.0454545 = 0.0117883 loss)
I0407 13:47:22.348968 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000204512 (* 0.0454545 = 9.29598e-06 loss)
I0407 13:47:22.348981 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000200643 (* 0.0454545 = 9.12016e-06 loss)
I0407 13:47:22.348995 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000201863 (* 0.0454545 = 9.1756e-06 loss)
I0407 13:47:22.349009 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000195747 (* 0.0454545 = 8.89757e-06 loss)
I0407 13:47:22.349023 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000197411 (* 0.0454545 = 8.97323e-06 loss)
I0407 13:47:22.349037 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000204239 (* 0.0454545 = 9.2836e-06 loss)
I0407 13:47:22.349051 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.00021676 (* 0.0454545 = 9.85271e-06 loss)
I0407 13:47:22.349066 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000212071 (* 0.0454545 = 9.63958e-06 loss)
I0407 13:47:22.349082 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000205359 (* 0.0454545 = 9.33451e-06 loss)
I0407 13:47:22.349097 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000195562 (* 0.0454545 = 8.8892e-06 loss)
I0407 13:47:22.349112 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.00017963 (* 0.0454545 = 8.16499e-06 loss)
I0407 13:47:22.349125 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000188456 (* 0.0454545 = 8.56619e-06 loss)
I0407 13:47:22.349138 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:47:22.349149 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000802239
I0407 13:47:22.349164 32304 sgd_solver.cpp:106] Iteration 35000, lr = 0.0093
I0407 13:48:33.937492 32304 solver.cpp:229] Iteration 35500, loss = 0.922157
I0407 13:48:33.937654 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 13:48:33.937675 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 13:48:33.937687 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 13:48:33.937700 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 13:48:33.937712 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 13:48:33.937724 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 13:48:33.937736 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 13:48:33.937747 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.8125
I0407 13:48:33.937759 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 13:48:33.937773 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 13:48:33.937795 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:48:33.937813 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:48:33.937826 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:48:33.937839 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:48:33.937849 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:48:33.937860 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:48:33.937875 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:48:33.937897 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:48:33.937911 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:48:33.937925 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:48:33.937937 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:48:33.937949 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:48:33.937965 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.25237 (* 0.0454545 = 0.147835 loss)
I0407 13:48:33.937979 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.61533 (* 0.0454545 = 0.164333 loss)
I0407 13:48:33.937994 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.4957 (* 0.0454545 = 0.158896 loss)
I0407 13:48:33.938007 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.44348 (* 0.0454545 = 0.156522 loss)
I0407 13:48:33.938020 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.30423 (* 0.0454545 = 0.150192 loss)
I0407 13:48:33.938035 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.77395 (* 0.0454545 = 0.126089 loss)
I0407 13:48:33.938047 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.49737 (* 0.0454545 = 0.0680622 loss)
I0407 13:48:33.938061 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.06296 (* 0.0454545 = 0.0483166 loss)
I0407 13:48:33.938074 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.572737 (* 0.0454545 = 0.0260335 loss)
I0407 13:48:33.938088 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.471125 (* 0.0454545 = 0.0214148 loss)
I0407 13:48:33.938102 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.00015981 (* 0.0454545 = 7.26409e-06 loss)
I0407 13:48:33.938118 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00015924 (* 0.0454545 = 7.23819e-06 loss)
I0407 13:48:33.938135 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000164567 (* 0.0454545 = 7.48034e-06 loss)
I0407 13:48:33.938149 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000157593 (* 0.0454545 = 7.16332e-06 loss)
I0407 13:48:33.938163 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.00015782 (* 0.0454545 = 7.17363e-06 loss)
I0407 13:48:33.938184 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000163391 (* 0.0454545 = 7.42686e-06 loss)
I0407 13:48:33.938210 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000171031 (* 0.0454545 = 7.77412e-06 loss)
I0407 13:48:33.938241 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000154238 (* 0.0454545 = 7.01081e-06 loss)
I0407 13:48:33.938256 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000172697 (* 0.0454545 = 7.84988e-06 loss)
I0407 13:48:33.938268 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000158463 (* 0.0454545 = 7.20288e-06 loss)
I0407 13:48:33.938282 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000165781 (* 0.0454545 = 7.5355e-06 loss)
I0407 13:48:33.938297 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000161482 (* 0.0454545 = 7.34011e-06 loss)
I0407 13:48:33.938308 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:48:33.938319 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000207774
I0407 13:48:33.938334 32304 sgd_solver.cpp:106] Iteration 35500, lr = 0.00929
I0407 13:49:46.382741 32304 solver.cpp:229] Iteration 36000, loss = 0.923775
I0407 13:49:46.382877 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:49:46.382897 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:49:46.382911 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:49:46.382926 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:49:46.382938 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 13:49:46.382951 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.53125
I0407 13:49:46.382962 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.875
I0407 13:49:46.382973 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 13:49:46.382985 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:49:46.382997 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:49:46.383008 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:49:46.383019 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:49:46.383030 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:49:46.383043 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:49:46.383054 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:49:46.383065 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:49:46.383077 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:49:46.383088 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:49:46.383100 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:49:46.383111 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:49:46.383122 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:49:46.383133 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:49:46.383150 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.86135 (* 0.0454545 = 0.130062 loss)
I0407 13:49:46.383164 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.14047 (* 0.0454545 = 0.142749 loss)
I0407 13:49:46.383178 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.15857 (* 0.0454545 = 0.143571 loss)
I0407 13:49:46.383191 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.18477 (* 0.0454545 = 0.144762 loss)
I0407 13:49:46.383205 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.96104 (* 0.0454545 = 0.134593 loss)
I0407 13:49:46.383219 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.71097 (* 0.0454545 = 0.0777712 loss)
I0407 13:49:46.383232 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.585727 (* 0.0454545 = 0.026624 loss)
I0407 13:49:46.383247 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.199673 (* 0.0454545 = 0.00907604 loss)
I0407 13:49:46.383261 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.280562 (* 0.0454545 = 0.0127528 loss)
I0407 13:49:46.383275 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.2295 (* 0.0454545 = 0.0104318 loss)
I0407 13:49:46.383290 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 9.47874e-05 (* 0.0454545 = 4.30852e-06 loss)
I0407 13:49:46.383303 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 9.88539e-05 (* 0.0454545 = 4.49336e-06 loss)
I0407 13:49:46.383332 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 9.48191e-05 (* 0.0454545 = 4.30996e-06 loss)
I0407 13:49:46.383348 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 9.59313e-05 (* 0.0454545 = 4.36052e-06 loss)
I0407 13:49:46.383363 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 9.53127e-05 (* 0.0454545 = 4.3324e-06 loss)
I0407 13:49:46.383376 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 9.58846e-05 (* 0.0454545 = 4.35839e-06 loss)
I0407 13:49:46.383390 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 9.40367e-05 (* 0.0454545 = 4.27439e-06 loss)
I0407 13:49:46.383422 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 9.77886e-05 (* 0.0454545 = 4.44494e-06 loss)
I0407 13:49:46.383438 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000108207 (* 0.0454545 = 4.91852e-06 loss)
I0407 13:49:46.383452 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 9.64122e-05 (* 0.0454545 = 4.38237e-06 loss)
I0407 13:49:46.383466 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 9.4398e-05 (* 0.0454545 = 4.29082e-06 loss)
I0407 13:49:46.383479 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 9.73846e-05 (* 0.0454545 = 4.42657e-06 loss)
I0407 13:49:46.383491 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:49:46.383503 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000458509
I0407 13:49:46.383518 32304 sgd_solver.cpp:106] Iteration 36000, lr = 0.00928
I0407 13:51:00.078649 32304 solver.cpp:229] Iteration 36500, loss = 0.921246
I0407 13:51:00.078814 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.15625
I0407 13:51:00.078835 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:51:00.078847 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:51:00.078860 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 13:51:00.078872 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.09375
I0407 13:51:00.078884 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.21875
I0407 13:51:00.078896 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.53125
I0407 13:51:00.078908 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:51:00.078922 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 13:51:00.078934 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 13:51:00.078946 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:51:00.078958 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:51:00.078970 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:51:00.078982 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:51:00.078994 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:51:00.079005 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:51:00.079016 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:51:00.079027 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:51:00.079040 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:51:00.079051 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:51:00.079061 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:51:00.079073 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:51:00.079089 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.77372 (* 0.0454545 = 0.126078 loss)
I0407 13:51:00.079103 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.21289 (* 0.0454545 = 0.14604 loss)
I0407 13:51:00.079118 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.22158 (* 0.0454545 = 0.146435 loss)
I0407 13:51:00.079131 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.19557 (* 0.0454545 = 0.145253 loss)
I0407 13:51:00.079145 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.2034 (* 0.0454545 = 0.145609 loss)
I0407 13:51:00.079159 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.40946 (* 0.0454545 = 0.154975 loss)
I0407 13:51:00.079172 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 2.30726 (* 0.0454545 = 0.104876 loss)
I0407 13:51:00.079186 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.489063 (* 0.0454545 = 0.0222302 loss)
I0407 13:51:00.079201 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.534736 (* 0.0454545 = 0.0243062 loss)
I0407 13:51:00.079215 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.362215 (* 0.0454545 = 0.0164643 loss)
I0407 13:51:00.079229 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.11348e-05 (* 0.0454545 = 1.86976e-06 loss)
I0407 13:51:00.079243 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 3.9568e-05 (* 0.0454545 = 1.79854e-06 loss)
I0407 13:51:00.079257 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 3.84113e-05 (* 0.0454545 = 1.74597e-06 loss)
I0407 13:51:00.079272 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 3.73196e-05 (* 0.0454545 = 1.69635e-06 loss)
I0407 13:51:00.079284 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.98195e-05 (* 0.0454545 = 1.80998e-06 loss)
I0407 13:51:00.079298 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.68223e-05 (* 0.0454545 = 1.67374e-06 loss)
I0407 13:51:00.079313 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.2463e-05 (* 0.0454545 = 1.93014e-06 loss)
I0407 13:51:00.079358 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 3.94694e-05 (* 0.0454545 = 1.79406e-06 loss)
I0407 13:51:00.079375 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.28262e-05 (* 0.0454545 = 1.94665e-06 loss)
I0407 13:51:00.079388 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 3.65503e-05 (* 0.0454545 = 1.66138e-06 loss)
I0407 13:51:00.079402 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 3.88695e-05 (* 0.0454545 = 1.7668e-06 loss)
I0407 13:51:00.079416 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.87707e-05 (* 0.0454545 = 1.76231e-06 loss)
I0407 13:51:00.079427 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:51:00.079439 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000178152
I0407 13:51:00.079453 32304 sgd_solver.cpp:106] Iteration 36500, lr = 0.00927
I0407 13:52:12.680615 32304 solver.cpp:229] Iteration 37000, loss = 0.919658
I0407 13:52:12.680763 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 13:52:12.680783 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 13:52:12.680796 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 13:52:12.680809 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 13:52:12.680821 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 13:52:12.680833 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.28125
I0407 13:52:12.680845 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 13:52:12.680856 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:52:12.680868 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 13:52:12.680881 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:52:12.680891 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:52:12.680903 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:52:12.680914 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:52:12.680929 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:52:12.680943 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:52:12.680953 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:52:12.680965 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:52:12.680976 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:52:12.680987 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:52:12.680999 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:52:12.681010 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:52:12.681021 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:52:12.681037 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.70364 (* 0.0454545 = 0.122893 loss)
I0407 13:52:12.681052 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.18581 (* 0.0454545 = 0.14481 loss)
I0407 13:52:12.681066 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.34463 (* 0.0454545 = 0.152029 loss)
I0407 13:52:12.681079 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.17205 (* 0.0454545 = 0.144184 loss)
I0407 13:52:12.681093 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.18285 (* 0.0454545 = 0.144675 loss)
I0407 13:52:12.681107 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.81065 (* 0.0454545 = 0.127757 loss)
I0407 13:52:12.681120 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.893346 (* 0.0454545 = 0.0406066 loss)
I0407 13:52:12.681133 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.478635 (* 0.0454545 = 0.0217562 loss)
I0407 13:52:12.681148 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.335785 (* 0.0454545 = 0.015263 loss)
I0407 13:52:12.681161 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.165635 (* 0.0454545 = 0.00752888 loss)
I0407 13:52:12.681176 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000242197 (* 0.0454545 = 1.1009e-05 loss)
I0407 13:52:12.681190 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00027421 (* 0.0454545 = 1.24641e-05 loss)
I0407 13:52:12.681205 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000260564 (* 0.0454545 = 1.18438e-05 loss)
I0407 13:52:12.681218 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000260457 (* 0.0454545 = 1.18389e-05 loss)
I0407 13:52:12.681232 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000250315 (* 0.0454545 = 1.1378e-05 loss)
I0407 13:52:12.681246 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000260603 (* 0.0454545 = 1.18456e-05 loss)
I0407 13:52:12.681264 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000257093 (* 0.0454545 = 1.1686e-05 loss)
I0407 13:52:12.681293 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000288404 (* 0.0454545 = 1.31093e-05 loss)
I0407 13:52:12.681308 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000238595 (* 0.0454545 = 1.08452e-05 loss)
I0407 13:52:12.681321 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000245699 (* 0.0454545 = 1.11681e-05 loss)
I0407 13:52:12.681335 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000240917 (* 0.0454545 = 1.09508e-05 loss)
I0407 13:52:12.681349 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000246062 (* 0.0454545 = 1.11846e-05 loss)
I0407 13:52:12.681361 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:52:12.681372 32304 solver.cpp:245]     Train net output #45: total_confidence = 7.73229e-05
I0407 13:52:12.681387 32304 sgd_solver.cpp:106] Iteration 37000, lr = 0.00926
I0407 13:53:25.045647 32304 solver.cpp:229] Iteration 37500, loss = 0.920059
I0407 13:53:25.045790 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:53:25.045814 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 13:53:25.045838 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 13:53:25.045858 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 13:53:25.045871 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.09375
I0407 13:53:25.045883 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.25
I0407 13:53:25.045894 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:53:25.045912 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 13:53:25.045938 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:53:25.045953 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:53:25.045965 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:53:25.045976 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:53:25.045989 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:53:25.046000 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:53:25.046010 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:53:25.046021 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:53:25.046032 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:53:25.046044 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:53:25.046056 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:53:25.046066 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:53:25.046077 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:53:25.046089 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:53:25.046105 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.97811 (* 0.0454545 = 0.135369 loss)
I0407 13:53:25.046119 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.14131 (* 0.0454545 = 0.142787 loss)
I0407 13:53:25.046133 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.25665 (* 0.0454545 = 0.148029 loss)
I0407 13:53:25.046146 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.23714 (* 0.0454545 = 0.147143 loss)
I0407 13:53:25.046160 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.32342 (* 0.0454545 = 0.151064 loss)
I0407 13:53:25.046174 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 3.34686 (* 0.0454545 = 0.15213 loss)
I0407 13:53:25.046186 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.33402 (* 0.0454545 = 0.0606373 loss)
I0407 13:53:25.046202 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.614059 (* 0.0454545 = 0.0279118 loss)
I0407 13:53:25.046229 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0753382 (* 0.0454545 = 0.00342446 loss)
I0407 13:53:25.046249 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.024135 (* 0.0454545 = 0.00109704 loss)
I0407 13:53:25.046264 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.44404e-05 (* 0.0454545 = 2.02002e-06 loss)
I0407 13:53:25.046279 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.63365e-05 (* 0.0454545 = 2.1062e-06 loss)
I0407 13:53:25.046294 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.12174e-05 (* 0.0454545 = 1.87352e-06 loss)
I0407 13:53:25.046308 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.29328e-05 (* 0.0454545 = 1.95149e-06 loss)
I0407 13:53:25.046322 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.84751e-05 (* 0.0454545 = 1.74887e-06 loss)
I0407 13:53:25.046336 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.09292e-05 (* 0.0454545 = 1.86042e-06 loss)
I0407 13:53:25.046350 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.13848e-05 (* 0.0454545 = 1.88113e-06 loss)
I0407 13:53:25.046381 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.33315e-05 (* 0.0454545 = 1.96961e-06 loss)
I0407 13:53:25.046396 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.31607e-05 (* 0.0454545 = 1.96185e-06 loss)
I0407 13:53:25.046411 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.32967e-05 (* 0.0454545 = 1.96803e-06 loss)
I0407 13:53:25.046424 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 3.835e-05 (* 0.0454545 = 1.74318e-06 loss)
I0407 13:53:25.046438 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.2273e-05 (* 0.0454545 = 1.9215e-06 loss)
I0407 13:53:25.046450 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:53:25.046463 32304 solver.cpp:245]     Train net output #45: total_confidence = 7.71073e-05
I0407 13:53:25.046476 32304 sgd_solver.cpp:106] Iteration 37500, lr = 0.00925
I0407 13:54:37.238514 32304 solver.cpp:229] Iteration 38000, loss = 0.918864
I0407 13:54:37.238639 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 13:54:37.238659 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:54:37.238672 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:54:37.238684 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 13:54:37.238697 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 13:54:37.238708 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 13:54:37.238720 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:54:37.238732 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 13:54:37.238744 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 13:54:37.238756 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 13:54:37.238767 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:54:37.238783 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:54:37.238807 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:54:37.238826 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:54:37.238837 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:54:37.238848 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:54:37.238860 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:54:37.238872 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:54:37.238883 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:54:37.238894 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:54:37.238905 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:54:37.238919 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:54:37.238936 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.31116 (* 0.0454545 = 0.150507 loss)
I0407 13:54:37.238950 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.40943 (* 0.0454545 = 0.154974 loss)
I0407 13:54:37.238965 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.65322 (* 0.0454545 = 0.166056 loss)
I0407 13:54:37.238977 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.19243 (* 0.0454545 = 0.145111 loss)
I0407 13:54:37.238991 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.04325 (* 0.0454545 = 0.138329 loss)
I0407 13:54:37.239006 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.55227 (* 0.0454545 = 0.116012 loss)
I0407 13:54:37.239018 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.35185 (* 0.0454545 = 0.0614479 loss)
I0407 13:54:37.239032 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.903946 (* 0.0454545 = 0.0410885 loss)
I0407 13:54:37.239045 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.213716 (* 0.0454545 = 0.00971438 loss)
I0407 13:54:37.239059 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.23395 (* 0.0454545 = 0.0106341 loss)
I0407 13:54:37.239073 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000166876 (* 0.0454545 = 7.58526e-06 loss)
I0407 13:54:37.239087 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000179639 (* 0.0454545 = 8.1654e-06 loss)
I0407 13:54:37.239101 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000158321 (* 0.0454545 = 7.19643e-06 loss)
I0407 13:54:37.239115 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000167206 (* 0.0454545 = 7.60026e-06 loss)
I0407 13:54:37.239130 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000155525 (* 0.0454545 = 7.06932e-06 loss)
I0407 13:54:37.239143 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000162734 (* 0.0454545 = 7.39698e-06 loss)
I0407 13:54:37.239157 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000178058 (* 0.0454545 = 8.09355e-06 loss)
I0407 13:54:37.239189 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000173682 (* 0.0454545 = 7.89466e-06 loss)
I0407 13:54:37.239204 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000177175 (* 0.0454545 = 8.0534e-06 loss)
I0407 13:54:37.239218 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000164726 (* 0.0454545 = 7.48753e-06 loss)
I0407 13:54:37.239233 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000156923 (* 0.0454545 = 7.13286e-06 loss)
I0407 13:54:37.239246 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000171455 (* 0.0454545 = 7.79342e-06 loss)
I0407 13:54:37.239259 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:54:37.239269 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000219415
I0407 13:54:37.239284 32304 sgd_solver.cpp:106] Iteration 38000, lr = 0.00924
I0407 13:55:49.822857 32304 solver.cpp:229] Iteration 38500, loss = 0.91155
I0407 13:55:49.822981 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 13:55:49.823001 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:55:49.823014 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:55:49.823026 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:55:49.823038 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.3125
I0407 13:55:49.823050 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 13:55:49.823061 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 13:55:49.823073 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.8125
I0407 13:55:49.823084 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 13:55:49.823096 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 13:55:49.823108 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:55:49.823120 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:55:49.823132 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:55:49.823143 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:55:49.823154 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:55:49.823165 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:55:49.823178 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:55:49.823189 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:55:49.823199 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:55:49.823210 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:55:49.823222 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:55:49.823233 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:55:49.823248 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.31858 (* 0.0454545 = 0.150844 loss)
I0407 13:55:49.823262 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.31532 (* 0.0454545 = 0.150696 loss)
I0407 13:55:49.823276 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.48703 (* 0.0454545 = 0.158501 loss)
I0407 13:55:49.823290 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.26466 (* 0.0454545 = 0.148394 loss)
I0407 13:55:49.823304 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.93028 (* 0.0454545 = 0.133195 loss)
I0407 13:55:49.823339 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.71106 (* 0.0454545 = 0.12323 loss)
I0407 13:55:49.823357 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.6732 (* 0.0454545 = 0.0760546 loss)
I0407 13:55:49.823371 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.947036 (* 0.0454545 = 0.0430471 loss)
I0407 13:55:49.823385 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.603464 (* 0.0454545 = 0.0274302 loss)
I0407 13:55:49.823400 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.431129 (* 0.0454545 = 0.0195967 loss)
I0407 13:55:49.823413 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.36394e-05 (* 0.0454545 = 3.80179e-06 loss)
I0407 13:55:49.823427 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.89906e-05 (* 0.0454545 = 3.59048e-06 loss)
I0407 13:55:49.823441 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 8.02296e-05 (* 0.0454545 = 3.6468e-06 loss)
I0407 13:55:49.823456 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.54057e-05 (* 0.0454545 = 3.88208e-06 loss)
I0407 13:55:49.823469 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 8.16421e-05 (* 0.0454545 = 3.711e-06 loss)
I0407 13:55:49.823483 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 8.49476e-05 (* 0.0454545 = 3.86125e-06 loss)
I0407 13:55:49.823498 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 8.41331e-05 (* 0.0454545 = 3.82423e-06 loss)
I0407 13:55:49.823529 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 9.04979e-05 (* 0.0454545 = 4.11354e-06 loss)
I0407 13:55:49.823544 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 8.42429e-05 (* 0.0454545 = 3.82922e-06 loss)
I0407 13:55:49.823557 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 8.31608e-05 (* 0.0454545 = 3.78003e-06 loss)
I0407 13:55:49.823571 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 7.78894e-05 (* 0.0454545 = 3.54043e-06 loss)
I0407 13:55:49.823585 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 8.03881e-05 (* 0.0454545 = 3.65401e-06 loss)
I0407 13:55:49.823597 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:55:49.823608 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000466444
I0407 13:55:49.823623 32304 sgd_solver.cpp:106] Iteration 38500, lr = 0.00923
I0407 13:57:01.975953 32304 solver.cpp:229] Iteration 39000, loss = 0.914318
I0407 13:57:01.976104 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 13:57:01.976125 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 13:57:01.976140 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:57:01.976151 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 13:57:01.976163 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 13:57:01.976176 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.53125
I0407 13:57:01.976187 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:57:01.976199 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:57:01.976212 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:57:01.976222 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:57:01.976234 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:57:01.976245 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:57:01.976256 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:57:01.976268 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:57:01.976279 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:57:01.976290 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:57:01.976301 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:57:01.976313 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:57:01.976325 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:57:01.976336 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:57:01.976347 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:57:01.976358 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:57:01.976374 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.28313 (* 0.0454545 = 0.149233 loss)
I0407 13:57:01.976388 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.39067 (* 0.0454545 = 0.154122 loss)
I0407 13:57:01.976402 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.35001 (* 0.0454545 = 0.152273 loss)
I0407 13:57:01.976416 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.43753 (* 0.0454545 = 0.156251 loss)
I0407 13:57:01.976430 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.92573 (* 0.0454545 = 0.132988 loss)
I0407 13:57:01.976444 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.93996 (* 0.0454545 = 0.0881802 loss)
I0407 13:57:01.976457 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.17273 (* 0.0454545 = 0.053306 loss)
I0407 13:57:01.976471 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.354498 (* 0.0454545 = 0.0161135 loss)
I0407 13:57:01.976485 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0183919 (* 0.0454545 = 0.000835997 loss)
I0407 13:57:01.976500 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00707137 (* 0.0454545 = 0.000321426 loss)
I0407 13:57:01.976514 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 7.40655e-05 (* 0.0454545 = 3.36661e-06 loss)
I0407 13:57:01.976528 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.47697e-05 (* 0.0454545 = 3.39862e-06 loss)
I0407 13:57:01.976542 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.10077e-05 (* 0.0454545 = 3.22762e-06 loss)
I0407 13:57:01.976557 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 7.44106e-05 (* 0.0454545 = 3.3823e-06 loss)
I0407 13:57:01.976570 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.63198e-05 (* 0.0454545 = 3.46908e-06 loss)
I0407 13:57:01.976584 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 7.02888e-05 (* 0.0454545 = 3.19495e-06 loss)
I0407 13:57:01.976598 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 7.37327e-05 (* 0.0454545 = 3.35149e-06 loss)
I0407 13:57:01.976639 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 6.86576e-05 (* 0.0454545 = 3.1208e-06 loss)
I0407 13:57:01.976655 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.43227e-05 (* 0.0454545 = 3.3783e-06 loss)
I0407 13:57:01.976668 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.18402e-05 (* 0.0454545 = 3.26546e-06 loss)
I0407 13:57:01.976682 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.74986e-05 (* 0.0454545 = 3.06812e-06 loss)
I0407 13:57:01.976696 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.37221e-05 (* 0.0454545 = 3.35101e-06 loss)
I0407 13:57:01.976708 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:57:01.976719 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000298672
I0407 13:57:01.976734 32304 sgd_solver.cpp:106] Iteration 39000, lr = 0.00922
I0407 13:58:14.786272 32304 solver.cpp:229] Iteration 39500, loss = 0.913471
I0407 13:58:14.786403 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 13:58:14.786423 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 13:58:14.786437 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:58:14.786448 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 13:58:14.786460 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.375
I0407 13:58:14.786473 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.53125
I0407 13:58:14.786484 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.9375
I0407 13:58:14.786495 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 1
I0407 13:58:14.786507 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:58:14.786519 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:58:14.786530 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:58:14.786541 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:58:14.786552 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:58:14.786563 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:58:14.786576 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:58:14.786586 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:58:14.786598 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:58:14.786609 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:58:14.786620 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:58:14.786633 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:58:14.786643 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:58:14.786655 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:58:14.786671 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.33736 (* 0.0454545 = 0.151698 loss)
I0407 13:58:14.786695 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.20066 (* 0.0454545 = 0.145485 loss)
I0407 13:58:14.786721 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.20141 (* 0.0454545 = 0.145519 loss)
I0407 13:58:14.786748 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.23152 (* 0.0454545 = 0.146887 loss)
I0407 13:58:14.786775 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.5764 (* 0.0454545 = 0.117109 loss)
I0407 13:58:14.786799 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.02523 (* 0.0454545 = 0.0920557 loss)
I0407 13:58:14.786814 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.483419 (* 0.0454545 = 0.0219736 loss)
I0407 13:58:14.786828 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.0618144 (* 0.0454545 = 0.00280975 loss)
I0407 13:58:14.786842 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0232591 (* 0.0454545 = 0.00105723 loss)
I0407 13:58:14.786856 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00867527 (* 0.0454545 = 0.000394331 loss)
I0407 13:58:14.786871 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.57836e-05 (* 0.0454545 = 3.89926e-06 loss)
I0407 13:58:14.786885 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 8.90448e-05 (* 0.0454545 = 4.04749e-06 loss)
I0407 13:58:14.786900 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 9.11906e-05 (* 0.0454545 = 4.14503e-06 loss)
I0407 13:58:14.786912 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.46787e-05 (* 0.0454545 = 3.84903e-06 loss)
I0407 13:58:14.786931 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 8.29302e-05 (* 0.0454545 = 3.76956e-06 loss)
I0407 13:58:14.786944 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 8.23858e-05 (* 0.0454545 = 3.74481e-06 loss)
I0407 13:58:14.786958 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 9.55305e-05 (* 0.0454545 = 4.3423e-06 loss)
I0407 13:58:14.786990 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 8.94467e-05 (* 0.0454545 = 4.06576e-06 loss)
I0407 13:58:14.787005 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 8.90792e-05 (* 0.0454545 = 4.04905e-06 loss)
I0407 13:58:14.787019 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 8.11414e-05 (* 0.0454545 = 3.68825e-06 loss)
I0407 13:58:14.787032 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 8.10345e-05 (* 0.0454545 = 3.68338e-06 loss)
I0407 13:58:14.787046 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 8.77619e-05 (* 0.0454545 = 3.98918e-06 loss)
I0407 13:58:14.787058 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:58:14.787070 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000421694
I0407 13:58:14.787084 32304 sgd_solver.cpp:106] Iteration 39500, lr = 0.00921
I0407 13:59:26.986021 32304 solver.cpp:338] Iteration 40000, Testing net (#0)
I0407 13:59:35.004961 32304 solver.cpp:393] Test loss: 0.844008
I0407 13:59:35.005020 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.107
I0407 13:59:35.005038 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.074
I0407 13:59:35.005050 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.087
I0407 13:59:35.005062 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.144
I0407 13:59:35.005074 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.234
I0407 13:59:35.005085 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.5
I0407 13:59:35.005097 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.889
I0407 13:59:35.005108 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 13:59:35.005120 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 13:59:35.005131 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 13:59:35.005143 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 13:59:35.005156 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 13:59:35.005167 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 13:59:35.005178 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 13:59:35.005189 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 13:59:35.005200 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 13:59:35.005211 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 13:59:35.005223 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 13:59:35.005234 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 13:59:35.005245 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 13:59:35.005256 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 13:59:35.005267 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 13:59:35.005283 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 3.22695 (* 0.0454545 = 0.146679 loss)
I0407 13:59:35.005297 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.16772 (* 0.0454545 = 0.143987 loss)
I0407 13:59:35.005311 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 3.20733 (* 0.0454545 = 0.145788 loss)
I0407 13:59:35.005324 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 3.10167 (* 0.0454545 = 0.140985 loss)
I0407 13:59:35.005338 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 2.94509 (* 0.0454545 = 0.133868 loss)
I0407 13:59:35.005352 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 1.93243 (* 0.0454545 = 0.0878378 loss)
I0407 13:59:35.005365 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.668483 (* 0.0454545 = 0.0303856 loss)
I0407 13:59:35.005378 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.245943 (* 0.0454545 = 0.0111792 loss)
I0407 13:59:35.005393 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0497487 (* 0.0454545 = 0.00226131 loss)
I0407 13:59:35.005406 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0221417 (* 0.0454545 = 0.00100644 loss)
I0407 13:59:35.005420 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 5.93416e-05 (* 0.0454545 = 2.69735e-06 loss)
I0407 13:59:35.005434 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 5.62443e-05 (* 0.0454545 = 2.55656e-06 loss)
I0407 13:59:35.005448 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 5.44241e-05 (* 0.0454545 = 2.47382e-06 loss)
I0407 13:59:35.005463 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 5.34802e-05 (* 0.0454545 = 2.43092e-06 loss)
I0407 13:59:35.005476 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 5.54529e-05 (* 0.0454545 = 2.52059e-06 loss)
I0407 13:59:35.005491 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 5.36453e-05 (* 0.0454545 = 2.43842e-06 loss)
I0407 13:59:35.005504 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 5.53946e-05 (* 0.0454545 = 2.51794e-06 loss)
I0407 13:59:35.005553 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 5.59895e-05 (* 0.0454545 = 2.54498e-06 loss)
I0407 13:59:35.005569 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 5.94053e-05 (* 0.0454545 = 2.70024e-06 loss)
I0407 13:59:35.005584 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 5.26847e-05 (* 0.0454545 = 2.39476e-06 loss)
I0407 13:59:35.005596 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 5.51093e-05 (* 0.0454545 = 2.50497e-06 loss)
I0407 13:59:35.005611 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 5.79437e-05 (* 0.0454545 = 2.63381e-06 loss)
I0407 13:59:35.005623 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0.001
I0407 13:59:35.005635 32304 solver.cpp:406]     Test net output #45: total_confidence = 0.000199412
I0407 13:59:35.040200 32304 solver.cpp:229] Iteration 40000, loss = 0.909501
I0407 13:59:35.040258 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.15625
I0407 13:59:35.040276 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 13:59:35.040287 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 13:59:35.040303 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 13:59:35.040316 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.34375
I0407 13:59:35.040328 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.53125
I0407 13:59:35.040340 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 13:59:35.040351 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 13:59:35.040364 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 13:59:35.040375 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 13:59:35.040386 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 13:59:35.040398 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 13:59:35.040411 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 13:59:35.040422 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 13:59:35.040433 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 13:59:35.040444 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 13:59:35.040455 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 13:59:35.040467 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 13:59:35.040478 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 13:59:35.040489 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 13:59:35.040500 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 13:59:35.040511 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 13:59:35.040526 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.61991 (* 0.0454545 = 0.119087 loss)
I0407 13:59:35.040541 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.23063 (* 0.0454545 = 0.146847 loss)
I0407 13:59:35.040555 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.13383 (* 0.0454545 = 0.142447 loss)
I0407 13:59:35.040568 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.00495 (* 0.0454545 = 0.136589 loss)
I0407 13:59:35.040581 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.17745 (* 0.0454545 = 0.14443 loss)
I0407 13:59:35.040594 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.82417 (* 0.0454545 = 0.082917 loss)
I0407 13:59:35.040608 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.00888 (* 0.0454545 = 0.0458584 loss)
I0407 13:59:35.040621 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.289548 (* 0.0454545 = 0.0131613 loss)
I0407 13:59:35.040635 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0496558 (* 0.0454545 = 0.00225708 loss)
I0407 13:59:35.040649 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0152194 (* 0.0454545 = 0.000691789 loss)
I0407 13:59:35.040688 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.58194e-05 (* 0.0454545 = 2.0827e-06 loss)
I0407 13:59:35.040704 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.76117e-05 (* 0.0454545 = 2.16417e-06 loss)
I0407 13:59:35.040717 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.29056e-05 (* 0.0454545 = 1.95026e-06 loss)
I0407 13:59:35.040731 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.46806e-05 (* 0.0454545 = 2.03093e-06 loss)
I0407 13:59:35.040745 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.49526e-05 (* 0.0454545 = 2.0433e-06 loss)
I0407 13:59:35.040760 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.21195e-05 (* 0.0454545 = 1.91452e-06 loss)
I0407 13:59:35.040774 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.26821e-05 (* 0.0454545 = 1.9401e-06 loss)
I0407 13:59:35.040788 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.68618e-05 (* 0.0454545 = 2.13008e-06 loss)
I0407 13:59:35.040802 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.25212e-05 (* 0.0454545 = 1.93278e-06 loss)
I0407 13:59:35.040817 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.53831e-05 (* 0.0454545 = 2.06287e-06 loss)
I0407 13:59:35.040829 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.0707e-05 (* 0.0454545 = 1.85032e-06 loss)
I0407 13:59:35.040843 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.4821e-05 (* 0.0454545 = 2.03732e-06 loss)
I0407 13:59:35.040855 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 13:59:35.040868 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000381905
I0407 13:59:35.040881 32304 sgd_solver.cpp:106] Iteration 40000, lr = 0.0092
I0407 14:00:47.123718 32304 solver.cpp:229] Iteration 40500, loss = 0.908417
I0407 14:00:47.123904 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 14:00:47.123929 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:00:47.123941 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0
I0407 14:00:47.123955 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 14:00:47.123966 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 14:00:47.123978 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 14:00:47.123991 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 14:00:47.124001 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 14:00:47.124013 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:00:47.124025 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:00:47.124037 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:00:47.124048 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:00:47.124060 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:00:47.124071 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:00:47.124083 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:00:47.124094 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:00:47.124105 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:00:47.124117 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:00:47.124128 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:00:47.124140 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:00:47.124151 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:00:47.124162 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:00:47.124178 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.0024 (* 0.0454545 = 0.136473 loss)
I0407 14:00:47.124192 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.21576 (* 0.0454545 = 0.146171 loss)
I0407 14:00:47.124207 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.35983 (* 0.0454545 = 0.152719 loss)
I0407 14:00:47.124220 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.19432 (* 0.0454545 = 0.145196 loss)
I0407 14:00:47.124234 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.04686 (* 0.0454545 = 0.138494 loss)
I0407 14:00:47.124248 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.55715 (* 0.0454545 = 0.116234 loss)
I0407 14:00:47.124261 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.58802 (* 0.0454545 = 0.0721827 loss)
I0407 14:00:47.124274 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.795162 (* 0.0454545 = 0.0361437 loss)
I0407 14:00:47.124289 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.439685 (* 0.0454545 = 0.0199857 loss)
I0407 14:00:47.124302 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.219688 (* 0.0454545 = 0.0099858 loss)
I0407 14:00:47.124316 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 2.47127e-05 (* 0.0454545 = 1.1233e-06 loss)
I0407 14:00:47.124331 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 2.30026e-05 (* 0.0454545 = 1.04558e-06 loss)
I0407 14:00:47.124346 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 2.2401e-05 (* 0.0454545 = 1.01823e-06 loss)
I0407 14:00:47.124358 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 2.23414e-05 (* 0.0454545 = 1.01552e-06 loss)
I0407 14:00:47.124372 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 2.36361e-05 (* 0.0454545 = 1.07437e-06 loss)
I0407 14:00:47.124387 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 2.10971e-05 (* 0.0454545 = 9.58958e-07 loss)
I0407 14:00:47.124410 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 2.43104e-05 (* 0.0454545 = 1.10502e-06 loss)
I0407 14:00:47.124440 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 2.3379e-05 (* 0.0454545 = 1.06268e-06 loss)
I0407 14:00:47.124455 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 2.6326e-05 (* 0.0454545 = 1.19664e-06 loss)
I0407 14:00:47.124469 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 2.21142e-05 (* 0.0454545 = 1.00519e-06 loss)
I0407 14:00:47.124482 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 2.3446e-05 (* 0.0454545 = 1.06573e-06 loss)
I0407 14:00:47.124496 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 2.24866e-05 (* 0.0454545 = 1.02212e-06 loss)
I0407 14:00:47.124508 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:00:47.124521 32304 solver.cpp:245]     Train net output #45: total_confidence = 2.26994e-06
I0407 14:00:47.124536 32304 sgd_solver.cpp:106] Iteration 40500, lr = 0.00919
I0407 14:01:59.800271 32304 solver.cpp:229] Iteration 41000, loss = 0.905246
I0407 14:01:59.800385 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 14:01:59.800403 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:01:59.800416 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 14:01:59.800428 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.1875
I0407 14:01:59.800441 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 14:01:59.800452 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5
I0407 14:01:59.800464 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 14:01:59.800477 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 14:01:59.800489 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:01:59.800500 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 14:01:59.800513 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:01:59.800523 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:01:59.800534 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:01:59.800545 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:01:59.800557 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:01:59.800568 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:01:59.800580 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:01:59.800591 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:01:59.800602 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:01:59.800613 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:01:59.800624 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:01:59.800637 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:01:59.800652 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.70247 (* 0.0454545 = 0.12284 loss)
I0407 14:01:59.800665 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.03304 (* 0.0454545 = 0.137865 loss)
I0407 14:01:59.800679 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.19791 (* 0.0454545 = 0.14536 loss)
I0407 14:01:59.800693 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.07534 (* 0.0454545 = 0.139788 loss)
I0407 14:01:59.800706 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.61905 (* 0.0454545 = 0.119048 loss)
I0407 14:01:59.800720 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.9144 (* 0.0454545 = 0.0870182 loss)
I0407 14:01:59.800734 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.6909 (* 0.0454545 = 0.0768589 loss)
I0407 14:01:59.800747 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.611211 (* 0.0454545 = 0.0277823 loss)
I0407 14:01:59.800760 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.427957 (* 0.0454545 = 0.0194526 loss)
I0407 14:01:59.800775 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.432832 (* 0.0454545 = 0.0196742 loss)
I0407 14:01:59.800788 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 6.96271e-05 (* 0.0454545 = 3.16487e-06 loss)
I0407 14:01:59.800802 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.07784e-05 (* 0.0454545 = 3.2172e-06 loss)
I0407 14:01:59.800817 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 6.44537e-05 (* 0.0454545 = 2.92971e-06 loss)
I0407 14:01:59.800830 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.75797e-05 (* 0.0454545 = 3.07181e-06 loss)
I0407 14:01:59.800843 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 6.93494e-05 (* 0.0454545 = 3.15225e-06 loss)
I0407 14:01:59.800858 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.26952e-05 (* 0.0454545 = 2.84978e-06 loss)
I0407 14:01:59.800871 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.36078e-05 (* 0.0454545 = 2.89126e-06 loss)
I0407 14:01:59.800902 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.14066e-05 (* 0.0454545 = 3.24576e-06 loss)
I0407 14:01:59.800917 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.71232e-05 (* 0.0454545 = 3.05105e-06 loss)
I0407 14:01:59.800931 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 6.55492e-05 (* 0.0454545 = 2.97951e-06 loss)
I0407 14:01:59.800945 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.51354e-05 (* 0.0454545 = 2.9607e-06 loss)
I0407 14:01:59.800958 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.84685e-05 (* 0.0454545 = 3.1122e-06 loss)
I0407 14:01:59.800971 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:01:59.800982 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000483439
I0407 14:01:59.800997 32304 sgd_solver.cpp:106] Iteration 41000, lr = 0.00918
I0407 14:03:11.888018 32304 solver.cpp:229] Iteration 41500, loss = 0.902875
I0407 14:03:11.888173 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 14:03:11.888193 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.15625
I0407 14:03:11.888207 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:03:11.888219 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 14:03:11.888232 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:03:11.888243 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 14:03:11.888255 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.59375
I0407 14:03:11.888267 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 14:03:11.888279 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:03:11.888291 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:03:11.888303 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:03:11.888314 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:03:11.888326 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:03:11.888339 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:03:11.888350 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:03:11.888362 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:03:11.888373 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:03:11.888386 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:03:11.888396 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:03:11.888408 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:03:11.888420 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:03:11.888432 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:03:11.888447 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.87965 (* 0.0454545 = 0.130893 loss)
I0407 14:03:11.888463 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.95749 (* 0.0454545 = 0.134431 loss)
I0407 14:03:11.888476 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.10052 (* 0.0454545 = 0.140933 loss)
I0407 14:03:11.888489 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.92719 (* 0.0454545 = 0.133054 loss)
I0407 14:03:11.888504 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.62017 (* 0.0454545 = 0.119099 loss)
I0407 14:03:11.888519 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.50024 (* 0.0454545 = 0.113647 loss)
I0407 14:03:11.888532 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.58789 (* 0.0454545 = 0.0721769 loss)
I0407 14:03:11.888545 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.891386 (* 0.0454545 = 0.0405176 loss)
I0407 14:03:11.888559 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.334731 (* 0.0454545 = 0.015215 loss)
I0407 14:03:11.888574 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.286904 (* 0.0454545 = 0.0130411 loss)
I0407 14:03:11.888588 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000214563 (* 0.0454545 = 9.75289e-06 loss)
I0407 14:03:11.888603 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000217428 (* 0.0454545 = 9.88308e-06 loss)
I0407 14:03:11.888617 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000202829 (* 0.0454545 = 9.21951e-06 loss)
I0407 14:03:11.888631 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000208036 (* 0.0454545 = 9.45619e-06 loss)
I0407 14:03:11.888645 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000209169 (* 0.0454545 = 9.50769e-06 loss)
I0407 14:03:11.888659 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000196821 (* 0.0454545 = 8.94639e-06 loss)
I0407 14:03:11.888674 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000205702 (* 0.0454545 = 9.35011e-06 loss)
I0407 14:03:11.888705 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.0002231 (* 0.0454545 = 1.01409e-05 loss)
I0407 14:03:11.888720 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000211231 (* 0.0454545 = 9.60142e-06 loss)
I0407 14:03:11.888734 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000203451 (* 0.0454545 = 9.24776e-06 loss)
I0407 14:03:11.888748 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000199879 (* 0.0454545 = 9.08542e-06 loss)
I0407 14:03:11.888761 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000211151 (* 0.0454545 = 9.59777e-06 loss)
I0407 14:03:11.888773 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:03:11.888785 32304 solver.cpp:245]     Train net output #45: total_confidence = 9.70183e-05
I0407 14:03:11.888799 32304 sgd_solver.cpp:106] Iteration 41500, lr = 0.00917
I0407 14:04:23.670900 32304 solver.cpp:229] Iteration 42000, loss = 0.903969
I0407 14:04:23.671028 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 14:04:23.671047 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:04:23.671061 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:04:23.671072 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:04:23.671084 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 14:04:23.671097 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5
I0407 14:04:23.671108 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.90625
I0407 14:04:23.671119 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 1
I0407 14:04:23.671130 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 14:04:23.671142 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:04:23.671154 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:04:23.671165 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:04:23.671177 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:04:23.671188 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:04:23.671200 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:04:23.671211 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:04:23.671222 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:04:23.671234 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:04:23.671246 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:04:23.671257 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:04:23.671268 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:04:23.671279 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:04:23.671295 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.04596 (* 0.0454545 = 0.138453 loss)
I0407 14:04:23.671309 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.26976 (* 0.0454545 = 0.148626 loss)
I0407 14:04:23.671341 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.32702 (* 0.0454545 = 0.151228 loss)
I0407 14:04:23.671357 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.15346 (* 0.0454545 = 0.143339 loss)
I0407 14:04:23.671370 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.07541 (* 0.0454545 = 0.139791 loss)
I0407 14:04:23.671385 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.09856 (* 0.0454545 = 0.095389 loss)
I0407 14:04:23.671398 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.637397 (* 0.0454545 = 0.0289726 loss)
I0407 14:04:23.671412 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.0861825 (* 0.0454545 = 0.00391739 loss)
I0407 14:04:23.671427 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0218657 (* 0.0454545 = 0.000993893 loss)
I0407 14:04:23.671442 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00951638 (* 0.0454545 = 0.000432563 loss)
I0407 14:04:23.671455 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000116907 (* 0.0454545 = 5.31398e-06 loss)
I0407 14:04:23.671469 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000124566 (* 0.0454545 = 5.66207e-06 loss)
I0407 14:04:23.671483 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000118037 (* 0.0454545 = 5.36533e-06 loss)
I0407 14:04:23.671496 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00011782 (* 0.0454545 = 5.35544e-06 loss)
I0407 14:04:23.671511 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000119887 (* 0.0454545 = 5.44942e-06 loss)
I0407 14:04:23.671525 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000110302 (* 0.0454545 = 5.01373e-06 loss)
I0407 14:04:23.671538 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000119708 (* 0.0454545 = 5.44125e-06 loss)
I0407 14:04:23.671571 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000123696 (* 0.0454545 = 5.62256e-06 loss)
I0407 14:04:23.671587 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000125698 (* 0.0454545 = 5.71355e-06 loss)
I0407 14:04:23.671600 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000118075 (* 0.0454545 = 5.36702e-06 loss)
I0407 14:04:23.671614 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000112373 (* 0.0454545 = 5.10785e-06 loss)
I0407 14:04:23.671628 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000119235 (* 0.0454545 = 5.41978e-06 loss)
I0407 14:04:23.671640 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:04:23.671653 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000319996
I0407 14:04:23.671668 32304 sgd_solver.cpp:106] Iteration 42000, lr = 0.00916
I0407 14:05:35.547704 32304 solver.cpp:229] Iteration 42500, loss = 0.90328
I0407 14:05:35.547850 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 14:05:35.547871 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:05:35.547884 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:05:35.547896 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.1875
I0407 14:05:35.547909 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:05:35.547922 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 14:05:35.547935 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 14:05:35.547947 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 14:05:35.547958 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:05:35.547971 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:05:35.547983 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:05:35.547996 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:05:35.548007 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:05:35.548019 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:05:35.548030 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:05:35.548043 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:05:35.548054 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:05:35.548065 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:05:35.548076 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:05:35.548089 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:05:35.548099 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:05:35.548110 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:05:35.548126 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.15597 (* 0.0454545 = 0.143453 loss)
I0407 14:05:35.548141 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.21097 (* 0.0454545 = 0.145953 loss)
I0407 14:05:35.548154 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.20779 (* 0.0454545 = 0.145809 loss)
I0407 14:05:35.548168 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.0043 (* 0.0454545 = 0.136559 loss)
I0407 14:05:35.548182 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.10484 (* 0.0454545 = 0.141129 loss)
I0407 14:05:35.548197 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.30298 (* 0.0454545 = 0.104681 loss)
I0407 14:05:35.548210 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.2696 (* 0.0454545 = 0.057709 loss)
I0407 14:05:35.548223 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.707992 (* 0.0454545 = 0.0321814 loss)
I0407 14:05:35.548238 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.137714 (* 0.0454545 = 0.00625971 loss)
I0407 14:05:35.548251 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0224387 (* 0.0454545 = 0.00101994 loss)
I0407 14:05:35.548266 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 5.49876e-05 (* 0.0454545 = 2.49943e-06 loss)
I0407 14:05:35.548280 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 5.9733e-05 (* 0.0454545 = 2.71514e-06 loss)
I0407 14:05:35.548295 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.44901e-05 (* 0.0454545 = 2.47682e-06 loss)
I0407 14:05:35.548308 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 5.33839e-05 (* 0.0454545 = 2.42654e-06 loss)
I0407 14:05:35.548323 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 5.48163e-05 (* 0.0454545 = 2.49165e-06 loss)
I0407 14:05:35.548337 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 5.20409e-05 (* 0.0454545 = 2.3655e-06 loss)
I0407 14:05:35.548352 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 5.5064e-05 (* 0.0454545 = 2.50291e-06 loss)
I0407 14:05:35.548379 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 5.77924e-05 (* 0.0454545 = 2.62693e-06 loss)
I0407 14:05:35.548394 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 5.42778e-05 (* 0.0454545 = 2.46717e-06 loss)
I0407 14:05:35.548408 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 5.37774e-05 (* 0.0454545 = 2.44443e-06 loss)
I0407 14:05:35.548423 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.97e-05 (* 0.0454545 = 2.25909e-06 loss)
I0407 14:05:35.548436 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 5.5275e-05 (* 0.0454545 = 2.5125e-06 loss)
I0407 14:05:35.548449 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:05:35.548460 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000510023
I0407 14:05:35.548475 32304 sgd_solver.cpp:106] Iteration 42500, lr = 0.00915
I0407 14:06:47.707361 32304 solver.cpp:229] Iteration 43000, loss = 0.901816
I0407 14:06:47.707517 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 14:06:47.707545 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:06:47.707567 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:06:47.707587 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.21875
I0407 14:06:47.707609 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:06:47.707629 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 14:06:47.707650 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 14:06:47.707671 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 14:06:47.707692 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 14:06:47.707713 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:06:47.707734 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:06:47.707753 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:06:47.707775 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:06:47.707797 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:06:47.707820 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:06:47.707841 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:06:47.707862 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:06:47.707883 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:06:47.707911 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:06:47.707937 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:06:47.707958 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:06:47.707986 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:06:47.708014 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.95954 (* 0.0454545 = 0.134524 loss)
I0407 14:06:47.708039 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.99881 (* 0.0454545 = 0.13631 loss)
I0407 14:06:47.708073 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.06728 (* 0.0454545 = 0.139422 loss)
I0407 14:06:47.708099 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.90998 (* 0.0454545 = 0.132272 loss)
I0407 14:06:47.708123 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.76962 (* 0.0454545 = 0.125892 loss)
I0407 14:06:47.708158 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.15633 (* 0.0454545 = 0.0980151 loss)
I0407 14:06:47.708183 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.851161 (* 0.0454545 = 0.0386891 loss)
I0407 14:06:47.708207 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.415586 (* 0.0454545 = 0.0188903 loss)
I0407 14:06:47.708233 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0186331 (* 0.0454545 = 0.000846958 loss)
I0407 14:06:47.708259 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00616803 (* 0.0454545 = 0.000280365 loss)
I0407 14:06:47.708286 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 5.85128e-05 (* 0.0454545 = 2.65967e-06 loss)
I0407 14:06:47.708310 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 5.82911e-05 (* 0.0454545 = 2.64959e-06 loss)
I0407 14:06:47.708335 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.81476e-05 (* 0.0454545 = 2.64308e-06 loss)
I0407 14:06:47.708361 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 5.81493e-05 (* 0.0454545 = 2.64315e-06 loss)
I0407 14:06:47.708387 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 5.67855e-05 (* 0.0454545 = 2.58116e-06 loss)
I0407 14:06:47.708413 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 5.20391e-05 (* 0.0454545 = 2.36541e-06 loss)
I0407 14:06:47.708438 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 5.5042e-05 (* 0.0454545 = 2.50191e-06 loss)
I0407 14:06:47.708487 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 5.05057e-05 (* 0.0454545 = 2.29572e-06 loss)
I0407 14:06:47.708515 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 5.78998e-05 (* 0.0454545 = 2.63181e-06 loss)
I0407 14:06:47.708546 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 5.42301e-05 (* 0.0454545 = 2.465e-06 loss)
I0407 14:06:47.708575 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 5.36971e-05 (* 0.0454545 = 2.44078e-06 loss)
I0407 14:06:47.708600 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 5.4336e-05 (* 0.0454545 = 2.46982e-06 loss)
I0407 14:06:47.708624 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:06:47.708657 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000556161
I0407 14:06:47.708683 32304 sgd_solver.cpp:106] Iteration 43000, lr = 0.00914
I0407 14:07:59.899155 32304 solver.cpp:229] Iteration 43500, loss = 0.900689
I0407 14:07:59.899287 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 14:07:59.899338 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:07:59.899368 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.15625
I0407 14:07:59.899389 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:07:59.899410 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:07:59.899431 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.53125
I0407 14:07:59.899452 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 14:07:59.899473 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 14:07:59.899494 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 14:07:59.899514 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:07:59.899536 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:07:59.899559 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:07:59.899583 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:07:59.899605 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:07:59.899626 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:07:59.899646 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:07:59.899667 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:07:59.899688 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:07:59.899710 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:07:59.899730 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:07:59.899750 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:07:59.899770 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:07:59.899798 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.82876 (* 0.0454545 = 0.12858 loss)
I0407 14:07:59.899824 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.09861 (* 0.0454545 = 0.140846 loss)
I0407 14:07:59.899850 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.00672 (* 0.0454545 = 0.136669 loss)
I0407 14:07:59.899875 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.06595 (* 0.0454545 = 0.139361 loss)
I0407 14:07:59.899900 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.99938 (* 0.0454545 = 0.136335 loss)
I0407 14:07:59.899929 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.13152 (* 0.0454545 = 0.0968871 loss)
I0407 14:07:59.899955 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.42683 (* 0.0454545 = 0.0648561 loss)
I0407 14:07:59.899979 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.880571 (* 0.0454545 = 0.0400259 loss)
I0407 14:07:59.900005 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.660581 (* 0.0454545 = 0.0300264 loss)
I0407 14:07:59.900032 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.368543 (* 0.0454545 = 0.016752 loss)
I0407 14:07:59.900058 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000129107 (* 0.0454545 = 5.86852e-06 loss)
I0407 14:07:59.900084 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00013399 (* 0.0454545 = 6.09046e-06 loss)
I0407 14:07:59.900110 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000134384 (* 0.0454545 = 6.10834e-06 loss)
I0407 14:07:59.900135 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000128313 (* 0.0454545 = 5.83241e-06 loss)
I0407 14:07:59.900161 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000122037 (* 0.0454545 = 5.54714e-06 loss)
I0407 14:07:59.900185 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000119328 (* 0.0454545 = 5.42399e-06 loss)
I0407 14:07:59.900212 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000118623 (* 0.0454545 = 5.39194e-06 loss)
I0407 14:07:59.900262 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000122817 (* 0.0454545 = 5.58261e-06 loss)
I0407 14:07:59.900292 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000127559 (* 0.0454545 = 5.79815e-06 loss)
I0407 14:07:59.900326 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000126107 (* 0.0454545 = 5.73214e-06 loss)
I0407 14:07:59.900353 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000121233 (* 0.0454545 = 5.51061e-06 loss)
I0407 14:07:59.900380 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.00012343 (* 0.0454545 = 5.61046e-06 loss)
I0407 14:07:59.900403 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:07:59.900424 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00047863
I0407 14:07:59.900449 32304 sgd_solver.cpp:106] Iteration 43500, lr = 0.00913
I0407 14:09:12.647136 32304 solver.cpp:229] Iteration 44000, loss = 0.895979
I0407 14:09:12.647275 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.21875
I0407 14:09:12.647295 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:09:12.647308 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:09:12.647320 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.21875
I0407 14:09:12.647332 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 14:09:12.647344 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 14:09:12.647356 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 14:09:12.647368 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:09:12.647380 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:09:12.647403 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:09:12.647418 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:09:12.647429 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:09:12.647440 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:09:12.647452 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:09:12.647464 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:09:12.647475 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:09:12.647486 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:09:12.647498 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:09:12.647510 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:09:12.647521 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:09:12.647531 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:09:12.647543 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:09:12.647558 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.08701 (* 0.0454545 = 0.140319 loss)
I0407 14:09:12.647573 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.37621 (* 0.0454545 = 0.153464 loss)
I0407 14:09:12.647588 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.32406 (* 0.0454545 = 0.151094 loss)
I0407 14:09:12.647601 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.30098 (* 0.0454545 = 0.150044 loss)
I0407 14:09:12.647614 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.80256 (* 0.0454545 = 0.127389 loss)
I0407 14:09:12.647629 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.37226 (* 0.0454545 = 0.10783 loss)
I0407 14:09:12.647642 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.54985 (* 0.0454545 = 0.0704478 loss)
I0407 14:09:12.647655 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.608544 (* 0.0454545 = 0.0276611 loss)
I0407 14:09:12.647670 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.497509 (* 0.0454545 = 0.022614 loss)
I0407 14:09:12.647683 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0163257 (* 0.0454545 = 0.000742077 loss)
I0407 14:09:12.647697 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000131653 (* 0.0454545 = 5.98425e-06 loss)
I0407 14:09:12.647711 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000150787 (* 0.0454545 = 6.85396e-06 loss)
I0407 14:09:12.647724 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000139013 (* 0.0454545 = 6.31879e-06 loss)
I0407 14:09:12.647738 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000130397 (* 0.0454545 = 5.92714e-06 loss)
I0407 14:09:12.647753 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000135927 (* 0.0454545 = 6.1785e-06 loss)
I0407 14:09:12.647766 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.0001361 (* 0.0454545 = 6.18638e-06 loss)
I0407 14:09:12.647780 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000136127 (* 0.0454545 = 6.18761e-06 loss)
I0407 14:09:12.647812 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000149379 (* 0.0454545 = 6.78994e-06 loss)
I0407 14:09:12.647827 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000144546 (* 0.0454545 = 6.57027e-06 loss)
I0407 14:09:12.647841 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000132166 (* 0.0454545 = 6.00756e-06 loss)
I0407 14:09:12.647855 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000122054 (* 0.0454545 = 5.54792e-06 loss)
I0407 14:09:12.647868 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000131629 (* 0.0454545 = 5.98314e-06 loss)
I0407 14:09:12.647881 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:09:12.647891 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000313848
I0407 14:09:12.647907 32304 sgd_solver.cpp:106] Iteration 44000, lr = 0.00912
I0407 14:10:25.344609 32304 solver.cpp:229] Iteration 44500, loss = 0.89885
I0407 14:10:25.344781 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 14:10:25.344801 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:10:25.344815 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:10:25.344827 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 14:10:25.344840 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 14:10:25.344851 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 14:10:25.344862 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 14:10:25.344874 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:10:25.344885 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:10:25.344897 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:10:25.344909 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:10:25.344924 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:10:25.344936 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:10:25.344949 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:10:25.344959 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:10:25.344971 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:10:25.344983 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:10:25.344995 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:10:25.345006 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:10:25.345017 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:10:25.345029 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:10:25.345041 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:10:25.345057 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.04466 (* 0.0454545 = 0.138394 loss)
I0407 14:10:25.345082 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.14068 (* 0.0454545 = 0.142758 loss)
I0407 14:10:25.345108 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.41568 (* 0.0454545 = 0.155258 loss)
I0407 14:10:25.345134 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.37207 (* 0.0454545 = 0.153276 loss)
I0407 14:10:25.345156 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.30803 (* 0.0454545 = 0.150365 loss)
I0407 14:10:25.345180 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.7752 (* 0.0454545 = 0.126145 loss)
I0407 14:10:25.345204 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.40303 (* 0.0454545 = 0.063774 loss)
I0407 14:10:25.345226 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.521199 (* 0.0454545 = 0.0236908 loss)
I0407 14:10:25.345240 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.43024 (* 0.0454545 = 0.0195563 loss)
I0407 14:10:25.345255 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00885201 (* 0.0454545 = 0.000402364 loss)
I0407 14:10:25.345270 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000124628 (* 0.0454545 = 5.66492e-06 loss)
I0407 14:10:25.345284 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000120352 (* 0.0454545 = 5.47053e-06 loss)
I0407 14:10:25.345299 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000122457 (* 0.0454545 = 5.56624e-06 loss)
I0407 14:10:25.345312 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000119008 (* 0.0454545 = 5.40944e-06 loss)
I0407 14:10:25.345326 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000109666 (* 0.0454545 = 4.98484e-06 loss)
I0407 14:10:25.345340 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000121948 (* 0.0454545 = 5.5431e-06 loss)
I0407 14:10:25.345355 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000123125 (* 0.0454545 = 5.59661e-06 loss)
I0407 14:10:25.345383 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000117233 (* 0.0454545 = 5.32878e-06 loss)
I0407 14:10:25.345398 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000121239 (* 0.0454545 = 5.51088e-06 loss)
I0407 14:10:25.345412 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000115931 (* 0.0454545 = 5.26959e-06 loss)
I0407 14:10:25.345425 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.00011828 (* 0.0454545 = 5.37639e-06 loss)
I0407 14:10:25.345440 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000118441 (* 0.0454545 = 5.38369e-06 loss)
I0407 14:10:25.345453 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:10:25.345463 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000130686
I0407 14:10:25.345479 32304 sgd_solver.cpp:106] Iteration 44500, lr = 0.00911
I0407 14:11:37.751458 32304 solver.cpp:338] Iteration 45000, Testing net (#0)
I0407 14:11:45.793267 32304 solver.cpp:393] Test loss: 0.815135
I0407 14:11:45.793330 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.169
I0407 14:11:45.793346 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.072
I0407 14:11:45.793359 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.101
I0407 14:11:45.793371 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.131
I0407 14:11:45.793382 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.24
I0407 14:11:45.793395 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.509
I0407 14:11:45.793406 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.894
I0407 14:11:45.793417 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 14:11:45.793428 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 14:11:45.793439 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 14:11:45.793452 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 14:11:45.793463 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 14:11:45.793474 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 14:11:45.793485 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 14:11:45.793496 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 14:11:45.793509 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 14:11:45.793519 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 14:11:45.793530 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 14:11:45.793542 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 14:11:45.793553 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 14:11:45.793565 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 14:11:45.793576 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 14:11:45.793592 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 3.07765 (* 0.0454545 = 0.139893 loss)
I0407 14:11:45.793606 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.13017 (* 0.0454545 = 0.14228 loss)
I0407 14:11:45.793620 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 3.13812 (* 0.0454545 = 0.142642 loss)
I0407 14:11:45.793633 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 3.03093 (* 0.0454545 = 0.13777 loss)
I0407 14:11:45.793648 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 2.85499 (* 0.0454545 = 0.129772 loss)
I0407 14:11:45.793661 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 1.82201 (* 0.0454545 = 0.0828185 loss)
I0407 14:11:45.793684 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.5857 (* 0.0454545 = 0.0266227 loss)
I0407 14:11:45.793699 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.220985 (* 0.0454545 = 0.0100448 loss)
I0407 14:11:45.793712 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.04956 (* 0.0454545 = 0.00225273 loss)
I0407 14:11:45.793725 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0216682 (* 0.0454545 = 0.00098492 loss)
I0407 14:11:45.793740 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 0.000103995 (* 0.0454545 = 4.72705e-06 loss)
I0407 14:11:45.793753 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 9.77353e-05 (* 0.0454545 = 4.44251e-06 loss)
I0407 14:11:45.793768 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 0.000100777 (* 0.0454545 = 4.58076e-06 loss)
I0407 14:11:45.793782 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 9.53461e-05 (* 0.0454545 = 4.33391e-06 loss)
I0407 14:11:45.793797 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 9.74283e-05 (* 0.0454545 = 4.42856e-06 loss)
I0407 14:11:45.793810 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 9.65076e-05 (* 0.0454545 = 4.38671e-06 loss)
I0407 14:11:45.793823 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 0.000100363 (* 0.0454545 = 4.56198e-06 loss)
I0407 14:11:45.793874 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 9.77951e-05 (* 0.0454545 = 4.44523e-06 loss)
I0407 14:11:45.793890 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 0.000104409 (* 0.0454545 = 4.74587e-06 loss)
I0407 14:11:45.793905 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 9.46184e-05 (* 0.0454545 = 4.30084e-06 loss)
I0407 14:11:45.793920 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 0.000101186 (* 0.0454545 = 4.59938e-06 loss)
I0407 14:11:45.793936 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 0.000103247 (* 0.0454545 = 4.69305e-06 loss)
I0407 14:11:45.793947 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0.001
I0407 14:11:45.793958 32304 solver.cpp:406]     Test net output #45: total_confidence = 0.000332729
I0407 14:11:45.828754 32304 solver.cpp:229] Iteration 45000, loss = 0.895029
I0407 14:11:45.828822 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 14:11:45.828840 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:11:45.828853 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 14:11:45.828866 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 14:11:45.828877 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 14:11:45.828889 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 14:11:45.828901 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 14:11:45.828912 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 14:11:45.828924 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:11:45.828935 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:11:45.828948 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:11:45.828958 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:11:45.828970 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:11:45.828981 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:11:45.828994 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:11:45.829005 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:11:45.829016 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:11:45.829027 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:11:45.829040 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:11:45.829051 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:11:45.829062 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:11:45.829077 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:11:45.829093 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.92188 (* 0.0454545 = 0.132813 loss)
I0407 14:11:45.829107 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.48629 (* 0.0454545 = 0.158468 loss)
I0407 14:11:45.829121 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.34175 (* 0.0454545 = 0.151898 loss)
I0407 14:11:45.829134 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.24386 (* 0.0454545 = 0.147448 loss)
I0407 14:11:45.829149 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.71737 (* 0.0454545 = 0.123517 loss)
I0407 14:11:45.829161 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.76338 (* 0.0454545 = 0.125608 loss)
I0407 14:11:45.829175 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.20365 (* 0.0454545 = 0.0547112 loss)
I0407 14:11:45.829188 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.190768 (* 0.0454545 = 0.00867128 loss)
I0407 14:11:45.829202 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.210451 (* 0.0454545 = 0.00956598 loss)
I0407 14:11:45.829244 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00756931 (* 0.0454545 = 0.00034406 loss)
I0407 14:11:45.829260 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 3.75439e-05 (* 0.0454545 = 1.70654e-06 loss)
I0407 14:11:45.829274 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 3.48611e-05 (* 0.0454545 = 1.5846e-06 loss)
I0407 14:11:45.829288 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 3.74677e-05 (* 0.0454545 = 1.70308e-06 loss)
I0407 14:11:45.829303 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 3.74343e-05 (* 0.0454545 = 1.70156e-06 loss)
I0407 14:11:45.829316 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.56435e-05 (* 0.0454545 = 1.62016e-06 loss)
I0407 14:11:45.829329 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.34679e-05 (* 0.0454545 = 1.52127e-06 loss)
I0407 14:11:45.829344 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 3.78999e-05 (* 0.0454545 = 1.72272e-06 loss)
I0407 14:11:45.829357 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 3.24394e-05 (* 0.0454545 = 1.47452e-06 loss)
I0407 14:11:45.829370 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 3.95801e-05 (* 0.0454545 = 1.79909e-06 loss)
I0407 14:11:45.829385 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 3.50475e-05 (* 0.0454545 = 1.59307e-06 loss)
I0407 14:11:45.829397 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 3.75961e-05 (* 0.0454545 = 1.70891e-06 loss)
I0407 14:11:45.829411 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.52375e-05 (* 0.0454545 = 1.60171e-06 loss)
I0407 14:11:45.829422 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:11:45.829434 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000194324
I0407 14:11:45.829448 32304 sgd_solver.cpp:106] Iteration 45000, lr = 0.0091
I0407 14:12:57.979995 32304 solver.cpp:229] Iteration 45500, loss = 0.887809
I0407 14:12:57.980130 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.03125
I0407 14:12:57.980159 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 14:12:57.980180 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.1875
I0407 14:12:57.980202 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 14:12:57.980223 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:12:57.980245 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 14:12:57.980267 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 14:12:57.980288 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 14:12:57.980309 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:12:57.980330 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:12:57.980350 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:12:57.980371 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:12:57.980394 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:12:57.980419 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:12:57.980442 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:12:57.980461 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:12:57.980482 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:12:57.980504 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:12:57.980523 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:12:57.980545 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:12:57.980566 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:12:57.980587 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:12:57.980613 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.35891 (* 0.0454545 = 0.152678 loss)
I0407 14:12:57.980638 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.30269 (* 0.0454545 = 0.150122 loss)
I0407 14:12:57.980664 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.1927 (* 0.0454545 = 0.145123 loss)
I0407 14:12:57.980690 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.44721 (* 0.0454545 = 0.156692 loss)
I0407 14:12:57.980715 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.04432 (* 0.0454545 = 0.138378 loss)
I0407 14:12:57.980741 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.56344 (* 0.0454545 = 0.11652 loss)
I0407 14:12:57.980770 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.11259 (* 0.0454545 = 0.0505722 loss)
I0407 14:12:57.980797 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.757077 (* 0.0454545 = 0.0344126 loss)
I0407 14:12:57.980821 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.285822 (* 0.0454545 = 0.0129919 loss)
I0407 14:12:57.980846 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.15578 (* 0.0454545 = 0.00708093 loss)
I0407 14:12:57.980872 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000170026 (* 0.0454545 = 7.72845e-06 loss)
I0407 14:12:57.980898 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00018316 (* 0.0454545 = 8.32546e-06 loss)
I0407 14:12:57.980924 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000181393 (* 0.0454545 = 8.24513e-06 loss)
I0407 14:12:57.980950 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000155601 (* 0.0454545 = 7.07278e-06 loss)
I0407 14:12:57.980976 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000163019 (* 0.0454545 = 7.40998e-06 loss)
I0407 14:12:57.981001 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000157224 (* 0.0454545 = 7.14656e-06 loss)
I0407 14:12:57.981027 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000172014 (* 0.0454545 = 7.81881e-06 loss)
I0407 14:12:57.981076 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000190678 (* 0.0454545 = 8.66719e-06 loss)
I0407 14:12:57.981103 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000161843 (* 0.0454545 = 7.35652e-06 loss)
I0407 14:12:57.981135 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000153625 (* 0.0454545 = 6.98295e-06 loss)
I0407 14:12:57.981165 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000167269 (* 0.0454545 = 7.60315e-06 loss)
I0407 14:12:57.981195 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000163627 (* 0.0454545 = 7.43759e-06 loss)
I0407 14:12:57.981217 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:12:57.981240 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000102689
I0407 14:12:57.981262 32304 sgd_solver.cpp:106] Iteration 45500, lr = 0.00909
I0407 14:14:10.027988 32304 solver.cpp:229] Iteration 46000, loss = 0.894094
I0407 14:14:10.028175 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.21875
I0407 14:14:10.028204 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.21875
I0407 14:14:10.028225 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:14:10.028247 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.21875
I0407 14:14:10.028270 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 14:14:10.028291 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.46875
I0407 14:14:10.028311 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 14:14:10.028332 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:14:10.028354 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:14:10.028376 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:14:10.028396 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:14:10.028416 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:14:10.028439 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:14:10.028461 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:14:10.028483 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:14:10.028504 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:14:10.028525 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:14:10.028547 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:14:10.028566 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:14:10.028586 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:14:10.028609 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:14:10.028630 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:14:10.028657 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.56829 (* 0.0454545 = 0.11674 loss)
I0407 14:14:10.028679 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.96698 (* 0.0454545 = 0.134863 loss)
I0407 14:14:10.028702 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.15282 (* 0.0454545 = 0.14331 loss)
I0407 14:14:10.028728 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.85076 (* 0.0454545 = 0.12958 loss)
I0407 14:14:10.028753 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.78256 (* 0.0454545 = 0.12648 loss)
I0407 14:14:10.028780 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.8958 (* 0.0454545 = 0.0861726 loss)
I0407 14:14:10.028806 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.07198 (* 0.0454545 = 0.0487262 loss)
I0407 14:14:10.028831 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.404085 (* 0.0454545 = 0.0183675 loss)
I0407 14:14:10.028856 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.333038 (* 0.0454545 = 0.0151381 loss)
I0407 14:14:10.028882 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.14581 (* 0.0454545 = 0.00662774 loss)
I0407 14:14:10.028908 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.09044e-05 (* 0.0454545 = 1.85929e-06 loss)
I0407 14:14:10.028937 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 3.97268e-05 (* 0.0454545 = 1.80577e-06 loss)
I0407 14:14:10.028964 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 3.86742e-05 (* 0.0454545 = 1.75792e-06 loss)
I0407 14:14:10.028991 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 3.57061e-05 (* 0.0454545 = 1.623e-06 loss)
I0407 14:14:10.029017 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.75845e-05 (* 0.0454545 = 1.70838e-06 loss)
I0407 14:14:10.029044 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.38657e-05 (* 0.0454545 = 1.53935e-06 loss)
I0407 14:14:10.029069 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 3.77446e-05 (* 0.0454545 = 1.71566e-06 loss)
I0407 14:14:10.029114 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 3.79066e-05 (* 0.0454545 = 1.72303e-06 loss)
I0407 14:14:10.029141 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 3.68091e-05 (* 0.0454545 = 1.67314e-06 loss)
I0407 14:14:10.029167 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 3.62819e-05 (* 0.0454545 = 1.64918e-06 loss)
I0407 14:14:10.029197 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 3.40855e-05 (* 0.0454545 = 1.54934e-06 loss)
I0407 14:14:10.029224 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.514e-05 (* 0.0454545 = 1.59727e-06 loss)
I0407 14:14:10.029247 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:14:10.029268 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00081676
I0407 14:14:10.029290 32304 sgd_solver.cpp:106] Iteration 46000, lr = 0.00908
I0407 14:15:22.015135 32304 solver.cpp:229] Iteration 46500, loss = 0.893956
I0407 14:15:22.015293 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 14:15:22.015314 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.15625
I0407 14:15:22.015329 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 14:15:22.015341 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 14:15:22.015353 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:15:22.015365 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.28125
I0407 14:15:22.015377 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 14:15:22.015388 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 14:15:22.015400 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:15:22.015424 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:15:22.015439 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:15:22.015450 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:15:22.015462 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:15:22.015475 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:15:22.015486 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:15:22.015497 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:15:22.015509 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:15:22.015521 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:15:22.015532 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:15:22.015543 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:15:22.015555 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:15:22.015566 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:15:22.015583 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.79066 (* 0.0454545 = 0.126848 loss)
I0407 14:15:22.015597 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.16584 (* 0.0454545 = 0.143902 loss)
I0407 14:15:22.015610 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.25606 (* 0.0454545 = 0.148003 loss)
I0407 14:15:22.015625 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.31815 (* 0.0454545 = 0.150825 loss)
I0407 14:15:22.015638 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.88481 (* 0.0454545 = 0.131128 loss)
I0407 14:15:22.015651 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.763 (* 0.0454545 = 0.125591 loss)
I0407 14:15:22.015666 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.70878 (* 0.0454545 = 0.0776719 loss)
I0407 14:15:22.015679 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.678533 (* 0.0454545 = 0.0308424 loss)
I0407 14:15:22.015694 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.185019 (* 0.0454545 = 0.00840995 loss)
I0407 14:15:22.015708 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.186598 (* 0.0454545 = 0.00848174 loss)
I0407 14:15:22.015722 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000302834 (* 0.0454545 = 1.37652e-05 loss)
I0407 14:15:22.015736 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000318264 (* 0.0454545 = 1.44666e-05 loss)
I0407 14:15:22.015750 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000315989 (* 0.0454545 = 1.43631e-05 loss)
I0407 14:15:22.015763 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000368418 (* 0.0454545 = 1.67463e-05 loss)
I0407 14:15:22.015777 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000326199 (* 0.0454545 = 1.48272e-05 loss)
I0407 14:15:22.015790 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000313607 (* 0.0454545 = 1.42549e-05 loss)
I0407 14:15:22.015805 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.00033116 (* 0.0454545 = 1.50527e-05 loss)
I0407 14:15:22.015838 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000329779 (* 0.0454545 = 1.49899e-05 loss)
I0407 14:15:22.015853 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000312088 (* 0.0454545 = 1.41858e-05 loss)
I0407 14:15:22.015867 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000294701 (* 0.0454545 = 1.33955e-05 loss)
I0407 14:15:22.015880 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000293731 (* 0.0454545 = 1.33514e-05 loss)
I0407 14:15:22.015894 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000310016 (* 0.0454545 = 1.40916e-05 loss)
I0407 14:15:22.015907 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:15:22.015918 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000581102
I0407 14:15:22.015933 32304 sgd_solver.cpp:106] Iteration 46500, lr = 0.00907
I0407 14:16:33.973507 32304 solver.cpp:229] Iteration 47000, loss = 0.89777
I0407 14:16:33.973629 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 14:16:33.973649 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.15625
I0407 14:16:33.973662 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:16:33.973675 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 14:16:33.973686 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 14:16:33.973697 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 14:16:33.973709 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 14:16:33.973721 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:16:33.973733 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:16:33.973744 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:16:33.973757 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:16:33.973767 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:16:33.973779 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:16:33.973790 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:16:33.973803 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:16:33.973814 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:16:33.973824 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:16:33.973836 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:16:33.973847 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:16:33.973858 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:16:33.973871 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:16:33.973891 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:16:33.973920 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.85117 (* 0.0454545 = 0.129599 loss)
I0407 14:16:33.973948 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.96495 (* 0.0454545 = 0.13477 loss)
I0407 14:16:33.973970 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.05021 (* 0.0454545 = 0.138646 loss)
I0407 14:16:33.973994 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.01989 (* 0.0454545 = 0.137268 loss)
I0407 14:16:33.974020 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.77949 (* 0.0454545 = 0.126341 loss)
I0407 14:16:33.974041 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.59186 (* 0.0454545 = 0.117812 loss)
I0407 14:16:33.974056 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.832971 (* 0.0454545 = 0.0378623 loss)
I0407 14:16:33.974071 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.469099 (* 0.0454545 = 0.0213227 loss)
I0407 14:16:33.974084 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.32642 (* 0.0454545 = 0.0148373 loss)
I0407 14:16:33.974098 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0377108 (* 0.0454545 = 0.00171413 loss)
I0407 14:16:33.974113 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000171775 (* 0.0454545 = 7.80795e-06 loss)
I0407 14:16:33.974128 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000179775 (* 0.0454545 = 8.17157e-06 loss)
I0407 14:16:33.974140 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000153662 (* 0.0454545 = 6.98463e-06 loss)
I0407 14:16:33.974155 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000156431 (* 0.0454545 = 7.11051e-06 loss)
I0407 14:16:33.974169 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000170914 (* 0.0454545 = 7.76881e-06 loss)
I0407 14:16:33.974184 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000153425 (* 0.0454545 = 6.97385e-06 loss)
I0407 14:16:33.974197 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000156835 (* 0.0454545 = 7.12885e-06 loss)
I0407 14:16:33.974230 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000155878 (* 0.0454545 = 7.08536e-06 loss)
I0407 14:16:33.974244 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000162395 (* 0.0454545 = 7.38159e-06 loss)
I0407 14:16:33.974258 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000159831 (* 0.0454545 = 7.26504e-06 loss)
I0407 14:16:33.974272 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000150387 (* 0.0454545 = 6.83578e-06 loss)
I0407 14:16:33.974287 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000160743 (* 0.0454545 = 7.3065e-06 loss)
I0407 14:16:33.974306 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:16:33.974331 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000916057
I0407 14:16:33.974361 32304 sgd_solver.cpp:106] Iteration 47000, lr = 0.00906
I0407 14:17:45.579366 32304 solver.cpp:229] Iteration 47500, loss = 0.890176
I0407 14:17:45.579500 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.21875
I0407 14:17:45.579519 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 14:17:45.579532 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:17:45.579545 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 14:17:45.579557 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 14:17:45.579568 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 14:17:45.579581 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 14:17:45.579592 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.75
I0407 14:17:45.579604 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:17:45.579615 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:17:45.579627 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:17:45.579638 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:17:45.579650 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:17:45.579661 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:17:45.579673 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:17:45.579684 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:17:45.579696 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:17:45.579707 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:17:45.579718 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:17:45.579730 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:17:45.579741 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:17:45.579753 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:17:45.579769 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.90908 (* 0.0454545 = 0.132231 loss)
I0407 14:17:45.579790 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.12059 (* 0.0454545 = 0.141845 loss)
I0407 14:17:45.579818 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.15932 (* 0.0454545 = 0.143605 loss)
I0407 14:17:45.579835 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.19704 (* 0.0454545 = 0.14532 loss)
I0407 14:17:45.579849 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.40064 (* 0.0454545 = 0.154575 loss)
I0407 14:17:45.579864 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.67436 (* 0.0454545 = 0.121562 loss)
I0407 14:17:45.579876 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.70567 (* 0.0454545 = 0.0775305 loss)
I0407 14:17:45.579890 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.96699 (* 0.0454545 = 0.0439541 loss)
I0407 14:17:45.579905 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.28585 (* 0.0454545 = 0.0129932 loss)
I0407 14:17:45.579921 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0536572 (* 0.0454545 = 0.00243896 loss)
I0407 14:17:45.579936 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.6072e-05 (* 0.0454545 = 3.91236e-06 loss)
I0407 14:17:45.579951 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 9.21322e-05 (* 0.0454545 = 4.18783e-06 loss)
I0407 14:17:45.579964 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 9.65072e-05 (* 0.0454545 = 4.38669e-06 loss)
I0407 14:17:45.579978 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 9.15407e-05 (* 0.0454545 = 4.16094e-06 loss)
I0407 14:17:45.579993 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 8.84521e-05 (* 0.0454545 = 4.02055e-06 loss)
I0407 14:17:45.580005 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 8.72338e-05 (* 0.0454545 = 3.96517e-06 loss)
I0407 14:17:45.580019 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 9.57877e-05 (* 0.0454545 = 4.35399e-06 loss)
I0407 14:17:45.580051 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 9.34527e-05 (* 0.0454545 = 4.24785e-06 loss)
I0407 14:17:45.580066 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 9.40916e-05 (* 0.0454545 = 4.27689e-06 loss)
I0407 14:17:45.580080 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.78435e-05 (* 0.0454545 = 3.53834e-06 loss)
I0407 14:17:45.580095 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 8.66267e-05 (* 0.0454545 = 3.93758e-06 loss)
I0407 14:17:45.580107 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 8.02659e-05 (* 0.0454545 = 3.64845e-06 loss)
I0407 14:17:45.580119 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:17:45.580132 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00197859
I0407 14:17:45.580145 32304 sgd_solver.cpp:106] Iteration 47500, lr = 0.00905
I0407 14:18:57.427652 32304 solver.cpp:229] Iteration 48000, loss = 0.888323
I0407 14:18:57.427810 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 14:18:57.427839 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.21875
I0407 14:18:57.427858 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 14:18:57.427880 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:18:57.427901 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 14:18:57.427925 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 14:18:57.427947 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 14:18:57.427968 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 14:18:57.427988 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:18:57.428007 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 14:18:57.428028 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:18:57.428048 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:18:57.428071 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:18:57.428093 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:18:57.428117 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:18:57.428139 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:18:57.428161 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:18:57.428182 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:18:57.428201 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:18:57.428222 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:18:57.428243 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:18:57.428263 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:18:57.428292 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.91296 (* 0.0454545 = 0.132407 loss)
I0407 14:18:57.428318 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.96151 (* 0.0454545 = 0.134614 loss)
I0407 14:18:57.428344 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.06899 (* 0.0454545 = 0.139499 loss)
I0407 14:18:57.428369 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.11442 (* 0.0454545 = 0.141565 loss)
I0407 14:18:57.428395 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.95085 (* 0.0454545 = 0.134129 loss)
I0407 14:18:57.428421 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.01231 (* 0.0454545 = 0.0914687 loss)
I0407 14:18:57.428445 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.21256 (* 0.0454545 = 0.0551163 loss)
I0407 14:18:57.428470 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.704093 (* 0.0454545 = 0.0320042 loss)
I0407 14:18:57.428496 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.375555 (* 0.0454545 = 0.0170707 loss)
I0407 14:18:57.428521 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.254523 (* 0.0454545 = 0.0115692 loss)
I0407 14:18:57.428547 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 3.00119e-05 (* 0.0454545 = 1.36418e-06 loss)
I0407 14:18:57.428573 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 2.87452e-05 (* 0.0454545 = 1.3066e-06 loss)
I0407 14:18:57.428598 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 2.97251e-05 (* 0.0454545 = 1.35114e-06 loss)
I0407 14:18:57.428625 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 2.93823e-05 (* 0.0454545 = 1.33556e-06 loss)
I0407 14:18:57.428650 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 2.85664e-05 (* 0.0454545 = 1.29847e-06 loss)
I0407 14:18:57.428676 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 2.71619e-05 (* 0.0454545 = 1.23463e-06 loss)
I0407 14:18:57.428701 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 3.01162e-05 (* 0.0454545 = 1.36892e-06 loss)
I0407 14:18:57.428745 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 2.90507e-05 (* 0.0454545 = 1.32049e-06 loss)
I0407 14:18:57.428772 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 3.2739e-05 (* 0.0454545 = 1.48814e-06 loss)
I0407 14:18:57.428798 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 2.74116e-05 (* 0.0454545 = 1.24598e-06 loss)
I0407 14:18:57.428827 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 2.76611e-05 (* 0.0454545 = 1.25732e-06 loss)
I0407 14:18:57.428853 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 2.84957e-05 (* 0.0454545 = 1.29526e-06 loss)
I0407 14:18:57.428876 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:18:57.428900 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000247302
I0407 14:18:57.428927 32304 sgd_solver.cpp:106] Iteration 48000, lr = 0.00904
I0407 14:20:09.899821 32304 solver.cpp:229] Iteration 48500, loss = 0.889778
I0407 14:20:09.899938 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 14:20:09.899957 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 14:20:09.899971 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:20:09.899983 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 14:20:09.899996 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.125
I0407 14:20:09.900007 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 14:20:09.900018 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 14:20:09.900030 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 14:20:09.900043 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 14:20:09.900054 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:20:09.900066 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:20:09.900080 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:20:09.900092 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:20:09.900104 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:20:09.900115 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:20:09.900127 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:20:09.900138 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:20:09.900151 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:20:09.900161 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:20:09.900172 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:20:09.900183 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:20:09.900195 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:20:09.900215 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.85868 (* 0.0454545 = 0.12994 loss)
I0407 14:20:09.900230 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.11641 (* 0.0454545 = 0.141655 loss)
I0407 14:20:09.900244 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.0441 (* 0.0454545 = 0.138368 loss)
I0407 14:20:09.900259 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.2679 (* 0.0454545 = 0.148541 loss)
I0407 14:20:09.900271 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.42046 (* 0.0454545 = 0.155475 loss)
I0407 14:20:09.900285 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.48962 (* 0.0454545 = 0.113165 loss)
I0407 14:20:09.900300 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.04765 (* 0.0454545 = 0.0476204 loss)
I0407 14:20:09.900312 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.634697 (* 0.0454545 = 0.0288498 loss)
I0407 14:20:09.900326 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.472007 (* 0.0454545 = 0.0214549 loss)
I0407 14:20:09.900339 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0277259 (* 0.0454545 = 0.00126027 loss)
I0407 14:20:09.900354 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000141184 (* 0.0454545 = 6.41744e-06 loss)
I0407 14:20:09.900377 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.00014292 (* 0.0454545 = 6.49637e-06 loss)
I0407 14:20:09.900406 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000148105 (* 0.0454545 = 6.73204e-06 loss)
I0407 14:20:09.900424 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000140633 (* 0.0454545 = 6.39241e-06 loss)
I0407 14:20:09.900439 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000141267 (* 0.0454545 = 6.42122e-06 loss)
I0407 14:20:09.900452 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.00012737 (* 0.0454545 = 5.78955e-06 loss)
I0407 14:20:09.900466 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000141716 (* 0.0454545 = 6.44165e-06 loss)
I0407 14:20:09.900498 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000135896 (* 0.0454545 = 6.17711e-06 loss)
I0407 14:20:09.900513 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000147483 (* 0.0454545 = 6.70377e-06 loss)
I0407 14:20:09.900527 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000132775 (* 0.0454545 = 6.03523e-06 loss)
I0407 14:20:09.900540 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.0001333 (* 0.0454545 = 6.05908e-06 loss)
I0407 14:20:09.900555 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000130694 (* 0.0454545 = 5.94062e-06 loss)
I0407 14:20:09.900568 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:20:09.900578 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000272426
I0407 14:20:09.900593 32304 sgd_solver.cpp:106] Iteration 48500, lr = 0.00903
I0407 14:21:22.290446 32304 solver.cpp:229] Iteration 49000, loss = 0.883549
I0407 14:21:22.290609 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.21875
I0407 14:21:22.290629 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:21:22.290643 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:21:22.290657 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 14:21:22.290668 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:21:22.290680 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.46875
I0407 14:21:22.290693 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 14:21:22.290704 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 14:21:22.290715 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:21:22.290727 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:21:22.290738 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:21:22.290750 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:21:22.290761 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:21:22.290772 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:21:22.290784 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:21:22.290796 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:21:22.290807 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:21:22.290818 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:21:22.290829 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:21:22.290841 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:21:22.290853 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:21:22.290864 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:21:22.290880 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.58242 (* 0.0454545 = 0.117383 loss)
I0407 14:21:22.290895 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.05894 (* 0.0454545 = 0.139043 loss)
I0407 14:21:22.290909 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.23273 (* 0.0454545 = 0.146942 loss)
I0407 14:21:22.290925 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.90017 (* 0.0454545 = 0.131826 loss)
I0407 14:21:22.290940 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.6366 (* 0.0454545 = 0.119845 loss)
I0407 14:21:22.290954 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.34357 (* 0.0454545 = 0.106526 loss)
I0407 14:21:22.290967 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.05815 (* 0.0454545 = 0.0480976 loss)
I0407 14:21:22.290982 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.353768 (* 0.0454545 = 0.0160804 loss)
I0407 14:21:22.290995 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.109617 (* 0.0454545 = 0.00498261 loss)
I0407 14:21:22.291009 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.113981 (* 0.0454545 = 0.00518097 loss)
I0407 14:21:22.291023 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 5.72506e-05 (* 0.0454545 = 2.6023e-06 loss)
I0407 14:21:22.291038 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 6.36292e-05 (* 0.0454545 = 2.89224e-06 loss)
I0407 14:21:22.291051 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.71562e-05 (* 0.0454545 = 2.59801e-06 loss)
I0407 14:21:22.291065 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 5.73255e-05 (* 0.0454545 = 2.6057e-06 loss)
I0407 14:21:22.291079 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 5.62744e-05 (* 0.0454545 = 2.55793e-06 loss)
I0407 14:21:22.291093 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 5.45833e-05 (* 0.0454545 = 2.48106e-06 loss)
I0407 14:21:22.291107 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 5.44417e-05 (* 0.0454545 = 2.47462e-06 loss)
I0407 14:21:22.291138 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 5.48235e-05 (* 0.0454545 = 2.49198e-06 loss)
I0407 14:21:22.291153 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 5.51007e-05 (* 0.0454545 = 2.50458e-06 loss)
I0407 14:21:22.291167 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 5.03375e-05 (* 0.0454545 = 2.28807e-06 loss)
I0407 14:21:22.291182 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 5.29941e-05 (* 0.0454545 = 2.40883e-06 loss)
I0407 14:21:22.291195 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 5.50992e-05 (* 0.0454545 = 2.50451e-06 loss)
I0407 14:21:22.291208 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:21:22.291218 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000606422
I0407 14:21:22.291234 32304 sgd_solver.cpp:106] Iteration 49000, lr = 0.00902
I0407 14:22:34.399117 32304 solver.cpp:229] Iteration 49500, loss = 0.881177
I0407 14:22:34.399260 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.15625
I0407 14:22:34.399279 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 14:22:34.399292 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:22:34.399304 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 14:22:34.399317 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 14:22:34.399329 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.28125
I0407 14:22:34.399341 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 14:22:34.399353 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:22:34.399365 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:22:34.399392 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:22:34.399406 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:22:34.399418 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:22:34.399430 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:22:34.399441 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:22:34.399453 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:22:34.399464 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:22:34.399476 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:22:34.399494 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:22:34.399505 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:22:34.399518 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:22:34.399528 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:22:34.399540 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:22:34.399564 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.0935 (* 0.0454545 = 0.140614 loss)
I0407 14:22:34.399580 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.32523 (* 0.0454545 = 0.151147 loss)
I0407 14:22:34.399595 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.53443 (* 0.0454545 = 0.160656 loss)
I0407 14:22:34.399622 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.42994 (* 0.0454545 = 0.155906 loss)
I0407 14:22:34.399646 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.24044 (* 0.0454545 = 0.147293 loss)
I0407 14:22:34.399667 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.65906 (* 0.0454545 = 0.120867 loss)
I0407 14:22:34.399682 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.32132 (* 0.0454545 = 0.0600598 loss)
I0407 14:22:34.399694 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.508779 (* 0.0454545 = 0.0231263 loss)
I0407 14:22:34.399708 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.407103 (* 0.0454545 = 0.0185047 loss)
I0407 14:22:34.399729 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0226558 (* 0.0454545 = 0.00102981 loss)
I0407 14:22:34.399744 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 7.70534e-05 (* 0.0454545 = 3.50243e-06 loss)
I0407 14:22:34.399758 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.616e-05 (* 0.0454545 = 3.46182e-06 loss)
I0407 14:22:34.399772 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.74462e-05 (* 0.0454545 = 3.52028e-06 loss)
I0407 14:22:34.399786 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 7.01044e-05 (* 0.0454545 = 3.18657e-06 loss)
I0407 14:22:34.399801 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.21501e-05 (* 0.0454545 = 3.27955e-06 loss)
I0407 14:22:34.399816 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 7.38156e-05 (* 0.0454545 = 3.35525e-06 loss)
I0407 14:22:34.399829 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.89749e-05 (* 0.0454545 = 3.13522e-06 loss)
I0407 14:22:34.399865 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.06429e-05 (* 0.0454545 = 3.21104e-06 loss)
I0407 14:22:34.399880 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.54559e-05 (* 0.0454545 = 3.42981e-06 loss)
I0407 14:22:34.399894 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 6.55174e-05 (* 0.0454545 = 2.97806e-06 loss)
I0407 14:22:34.399907 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 7.1365e-05 (* 0.0454545 = 3.24386e-06 loss)
I0407 14:22:34.399924 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.8617e-05 (* 0.0454545 = 3.11895e-06 loss)
I0407 14:22:34.399937 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:22:34.399950 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000111713
I0407 14:22:34.399963 32304 sgd_solver.cpp:106] Iteration 49500, lr = 0.00901
I0407 14:23:46.371624 32304 solver.cpp:338] Iteration 50000, Testing net (#0)
I0407 14:23:54.356832 32304 solver.cpp:393] Test loss: 0.784114
I0407 14:23:54.356894 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.228
I0407 14:23:54.356911 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.089
I0407 14:23:54.356927 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.104
I0407 14:23:54.356940 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.142
I0407 14:23:54.356951 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.236
I0407 14:23:54.356963 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.523
I0407 14:23:54.356974 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.892
I0407 14:23:54.356986 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 14:23:54.356997 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 14:23:54.357008 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 14:23:54.357019 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 14:23:54.357030 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 14:23:54.357043 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 14:23:54.357053 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 14:23:54.357064 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 14:23:54.357075 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 14:23:54.357086 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 14:23:54.357098 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 14:23:54.357110 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 14:23:54.357125 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 14:23:54.357146 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 14:23:54.357164 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 14:23:54.357189 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 2.90497 (* 0.0454545 = 0.132044 loss)
I0407 14:23:54.357213 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.06394 (* 0.0454545 = 0.13927 loss)
I0407 14:23:54.357240 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 3.05483 (* 0.0454545 = 0.138856 loss)
I0407 14:23:54.357260 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 2.95996 (* 0.0454545 = 0.134544 loss)
I0407 14:23:54.357275 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 2.72249 (* 0.0454545 = 0.12375 loss)
I0407 14:23:54.357287 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 1.74271 (* 0.0454545 = 0.0792142 loss)
I0407 14:23:54.357301 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.521814 (* 0.0454545 = 0.0237188 loss)
I0407 14:23:54.357314 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.206878 (* 0.0454545 = 0.00940354 loss)
I0407 14:23:54.357328 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0498564 (* 0.0454545 = 0.0022662 loss)
I0407 14:23:54.357342 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0218698 (* 0.0454545 = 0.000994083 loss)
I0407 14:23:54.357357 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 0.000103864 (* 0.0454545 = 4.72109e-06 loss)
I0407 14:23:54.357369 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 0.000101576 (* 0.0454545 = 4.61708e-06 loss)
I0407 14:23:54.357383 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 9.85107e-05 (* 0.0454545 = 4.47776e-06 loss)
I0407 14:23:54.357398 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 9.75919e-05 (* 0.0454545 = 4.436e-06 loss)
I0407 14:23:54.357411 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 9.8213e-05 (* 0.0454545 = 4.46423e-06 loss)
I0407 14:23:54.357424 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 9.29934e-05 (* 0.0454545 = 4.22697e-06 loss)
I0407 14:23:54.357439 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 9.52293e-05 (* 0.0454545 = 4.3286e-06 loss)
I0407 14:23:54.357488 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 9.31758e-05 (* 0.0454545 = 4.23526e-06 loss)
I0407 14:23:54.357503 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 0.00010233 (* 0.0454545 = 4.65135e-06 loss)
I0407 14:23:54.357517 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 9.1415e-05 (* 0.0454545 = 4.15523e-06 loss)
I0407 14:23:54.357530 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 9.43549e-05 (* 0.0454545 = 4.28886e-06 loss)
I0407 14:23:54.357544 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 9.93736e-05 (* 0.0454545 = 4.51698e-06 loss)
I0407 14:23:54.357555 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0.006
I0407 14:23:54.357568 32304 solver.cpp:406]     Test net output #45: total_confidence = 0.000677494
I0407 14:23:54.392468 32304 solver.cpp:229] Iteration 50000, loss = 0.881647
I0407 14:23:54.392527 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 14:23:54.392544 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:23:54.392556 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:23:54.392570 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 14:23:54.392581 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 14:23:54.392593 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 14:23:54.392604 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 14:23:54.392616 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 14:23:54.392627 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:23:54.392639 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:23:54.392652 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:23:54.392663 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:23:54.392674 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:23:54.392685 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:23:54.392702 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:23:54.392724 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:23:54.392745 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:23:54.392765 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:23:54.392786 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:23:54.392809 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:23:54.392824 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:23:54.392835 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:23:54.392850 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.02019 (* 0.0454545 = 0.137281 loss)
I0407 14:23:54.392865 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.15497 (* 0.0454545 = 0.143408 loss)
I0407 14:23:54.392879 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.29793 (* 0.0454545 = 0.149906 loss)
I0407 14:23:54.392892 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.01229 (* 0.0454545 = 0.136922 loss)
I0407 14:23:54.392906 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.70362 (* 0.0454545 = 0.122892 loss)
I0407 14:23:54.392920 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.46548 (* 0.0454545 = 0.112067 loss)
I0407 14:23:54.392933 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.42976 (* 0.0454545 = 0.064989 loss)
I0407 14:23:54.392947 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.901443 (* 0.0454545 = 0.0409747 loss)
I0407 14:23:54.392961 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.197479 (* 0.0454545 = 0.00897634 loss)
I0407 14:23:54.393000 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.180178 (* 0.0454545 = 0.00818991 loss)
I0407 14:23:54.393016 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 6.44119e-05 (* 0.0454545 = 2.92781e-06 loss)
I0407 14:23:54.393030 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 6.62301e-05 (* 0.0454545 = 3.01046e-06 loss)
I0407 14:23:54.393044 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.98493e-05 (* 0.0454545 = 2.72042e-06 loss)
I0407 14:23:54.393059 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.59157e-05 (* 0.0454545 = 2.99617e-06 loss)
I0407 14:23:54.393075 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 6.14721e-05 (* 0.0454545 = 2.79419e-06 loss)
I0407 14:23:54.393090 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 5.52287e-05 (* 0.0454545 = 2.5104e-06 loss)
I0407 14:23:54.393103 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 5.98396e-05 (* 0.0454545 = 2.71998e-06 loss)
I0407 14:23:54.393117 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 6.04865e-05 (* 0.0454545 = 2.74939e-06 loss)
I0407 14:23:54.393131 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.04693e-05 (* 0.0454545 = 2.7486e-06 loss)
I0407 14:23:54.393144 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 5.66562e-05 (* 0.0454545 = 2.57528e-06 loss)
I0407 14:23:54.393158 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 5.62907e-05 (* 0.0454545 = 2.55867e-06 loss)
I0407 14:23:54.393172 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.21655e-05 (* 0.0454545 = 2.82571e-06 loss)
I0407 14:23:54.393187 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:23:54.393210 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000384079
I0407 14:23:54.393236 32304 sgd_solver.cpp:106] Iteration 50000, lr = 0.009
I0407 14:25:06.112782 32304 solver.cpp:229] Iteration 50500, loss = 0.881568
I0407 14:25:06.112934 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 14:25:06.112956 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 14:25:06.112968 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:25:06.112980 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:25:06.112993 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 14:25:06.113004 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 14:25:06.113016 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 14:25:06.113029 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 14:25:06.113040 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:25:06.113051 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:25:06.113064 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:25:06.113075 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:25:06.113086 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:25:06.113097 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:25:06.113109 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:25:06.113121 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:25:06.113132 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:25:06.113144 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:25:06.113155 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:25:06.113167 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:25:06.113178 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:25:06.113189 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:25:06.113205 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.65594 (* 0.0454545 = 0.120725 loss)
I0407 14:25:06.113219 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.07905 (* 0.0454545 = 0.139957 loss)
I0407 14:25:06.113234 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 2.97997 (* 0.0454545 = 0.135453 loss)
I0407 14:25:06.113247 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.02931 (* 0.0454545 = 0.137696 loss)
I0407 14:25:06.113260 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.85487 (* 0.0454545 = 0.129767 loss)
I0407 14:25:06.113275 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.22711 (* 0.0454545 = 0.101232 loss)
I0407 14:25:06.113287 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.959122 (* 0.0454545 = 0.0435965 loss)
I0407 14:25:06.113301 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.125497 (* 0.0454545 = 0.0057044 loss)
I0407 14:25:06.113315 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.133611 (* 0.0454545 = 0.00607324 loss)
I0407 14:25:06.113329 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0084647 (* 0.0454545 = 0.000384759 loss)
I0407 14:25:06.113343 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 6.24607e-05 (* 0.0454545 = 2.83912e-06 loss)
I0407 14:25:06.113358 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 6.01236e-05 (* 0.0454545 = 2.73289e-06 loss)
I0407 14:25:06.113371 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.90169e-05 (* 0.0454545 = 2.68259e-06 loss)
I0407 14:25:06.113385 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 5.88489e-05 (* 0.0454545 = 2.67495e-06 loss)
I0407 14:25:06.113399 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 5.84716e-05 (* 0.0454545 = 2.6578e-06 loss)
I0407 14:25:06.113414 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.05231e-05 (* 0.0454545 = 2.75105e-06 loss)
I0407 14:25:06.113427 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 5.72584e-05 (* 0.0454545 = 2.60265e-06 loss)
I0407 14:25:06.113459 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 5.68933e-05 (* 0.0454545 = 2.58606e-06 loss)
I0407 14:25:06.113474 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.30157e-05 (* 0.0454545 = 2.86435e-06 loss)
I0407 14:25:06.113488 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 5.97757e-05 (* 0.0454545 = 2.71708e-06 loss)
I0407 14:25:06.113502 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 5.91347e-05 (* 0.0454545 = 2.68794e-06 loss)
I0407 14:25:06.113517 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 5.85312e-05 (* 0.0454545 = 2.66051e-06 loss)
I0407 14:25:06.113528 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:25:06.113540 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000295619
I0407 14:25:06.113554 32304 sgd_solver.cpp:106] Iteration 50500, lr = 0.00899
I0407 14:26:18.239460 32304 solver.cpp:229] Iteration 51000, loss = 0.878018
I0407 14:26:18.239629 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 14:26:18.239655 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:26:18.239668 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:26:18.239681 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 14:26:18.239693 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 14:26:18.239704 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 14:26:18.239717 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 14:26:18.239728 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 14:26:18.239740 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:26:18.239753 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:26:18.239764 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:26:18.239776 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:26:18.239789 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:26:18.239799 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:26:18.239811 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:26:18.239822 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:26:18.239835 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:26:18.239850 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:26:18.239862 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:26:18.239873 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:26:18.239886 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:26:18.239897 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:26:18.239924 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.10144 (* 0.0454545 = 0.140975 loss)
I0407 14:26:18.239939 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.35828 (* 0.0454545 = 0.152649 loss)
I0407 14:26:18.239953 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.25209 (* 0.0454545 = 0.147822 loss)
I0407 14:26:18.239967 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.19672 (* 0.0454545 = 0.145305 loss)
I0407 14:26:18.239981 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.02362 (* 0.0454545 = 0.137437 loss)
I0407 14:26:18.239995 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.55391 (* 0.0454545 = 0.116087 loss)
I0407 14:26:18.240008 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.44766 (* 0.0454545 = 0.0658028 loss)
I0407 14:26:18.240025 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.828571 (* 0.0454545 = 0.0376623 loss)
I0407 14:26:18.240039 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.163226 (* 0.0454545 = 0.00741936 loss)
I0407 14:26:18.240053 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.130193 (* 0.0454545 = 0.00591786 loss)
I0407 14:26:18.240068 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000109243 (* 0.0454545 = 4.96561e-06 loss)
I0407 14:26:18.240087 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000113606 (* 0.0454545 = 5.16391e-06 loss)
I0407 14:26:18.240102 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000101866 (* 0.0454545 = 4.63029e-06 loss)
I0407 14:26:18.240115 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 9.81768e-05 (* 0.0454545 = 4.46258e-06 loss)
I0407 14:26:18.240129 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000104562 (* 0.0454545 = 4.75284e-06 loss)
I0407 14:26:18.240150 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 9.82664e-05 (* 0.0454545 = 4.46666e-06 loss)
I0407 14:26:18.240164 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000102711 (* 0.0454545 = 4.66867e-06 loss)
I0407 14:26:18.240201 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000112087 (* 0.0454545 = 5.09487e-06 loss)
I0407 14:26:18.240216 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000105197 (* 0.0454545 = 4.78167e-06 loss)
I0407 14:26:18.240231 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.00010228 (* 0.0454545 = 4.6491e-06 loss)
I0407 14:26:18.240244 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 9.58124e-05 (* 0.0454545 = 4.35511e-06 loss)
I0407 14:26:18.240258 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000100848 (* 0.0454545 = 4.58398e-06 loss)
I0407 14:26:18.240270 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:26:18.240283 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000418362
I0407 14:26:18.240298 32304 sgd_solver.cpp:106] Iteration 51000, lr = 0.00898
I0407 14:27:30.177147 32304 solver.cpp:229] Iteration 51500, loss = 0.872754
I0407 14:27:30.177314 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.28125
I0407 14:27:30.177335 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:27:30.177348 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:27:30.177361 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:27:30.177372 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:27:30.177384 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 14:27:30.177402 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 14:27:30.177415 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 14:27:30.177428 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:27:30.177439 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:27:30.177450 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:27:30.177462 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:27:30.177474 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:27:30.177485 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:27:30.177505 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:27:30.177527 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:27:30.177547 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:27:30.177561 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:27:30.177572 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:27:30.177583 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:27:30.177594 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:27:30.177609 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:27:30.177625 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.63536 (* 0.0454545 = 0.119789 loss)
I0407 14:27:30.177640 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.93273 (* 0.0454545 = 0.133306 loss)
I0407 14:27:30.177654 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.05911 (* 0.0454545 = 0.13905 loss)
I0407 14:27:30.177669 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.77584 (* 0.0454545 = 0.126175 loss)
I0407 14:27:30.177681 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.77527 (* 0.0454545 = 0.126149 loss)
I0407 14:27:30.177695 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.07404 (* 0.0454545 = 0.0942744 loss)
I0407 14:27:30.177709 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.07143 (* 0.0454545 = 0.0487011 loss)
I0407 14:27:30.177723 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.180281 (* 0.0454545 = 0.00819459 loss)
I0407 14:27:30.177737 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.113333 (* 0.0454545 = 0.00515148 loss)
I0407 14:27:30.177752 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00917779 (* 0.0454545 = 0.000417173 loss)
I0407 14:27:30.177767 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 6.15253e-05 (* 0.0454545 = 2.7966e-06 loss)
I0407 14:27:30.177780 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 5.95025e-05 (* 0.0454545 = 2.70466e-06 loss)
I0407 14:27:30.177794 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.69095e-05 (* 0.0454545 = 2.58679e-06 loss)
I0407 14:27:30.177809 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 5.40846e-05 (* 0.0454545 = 2.45839e-06 loss)
I0407 14:27:30.177821 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 5.70082e-05 (* 0.0454545 = 2.59128e-06 loss)
I0407 14:27:30.177835 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 5.35241e-05 (* 0.0454545 = 2.43292e-06 loss)
I0407 14:27:30.177850 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 5.61748e-05 (* 0.0454545 = 2.5534e-06 loss)
I0407 14:27:30.177877 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 6.47104e-05 (* 0.0454545 = 2.94138e-06 loss)
I0407 14:27:30.177906 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 5.50215e-05 (* 0.0454545 = 2.50098e-06 loss)
I0407 14:27:30.177922 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.97945e-05 (* 0.0454545 = 2.26339e-06 loss)
I0407 14:27:30.177937 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 5.20801e-05 (* 0.0454545 = 2.36728e-06 loss)
I0407 14:27:30.177950 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 5.22421e-05 (* 0.0454545 = 2.37464e-06 loss)
I0407 14:27:30.177963 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:27:30.177973 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00012495
I0407 14:27:30.177989 32304 sgd_solver.cpp:106] Iteration 51500, lr = 0.00897
I0407 14:28:42.500881 32304 solver.cpp:229] Iteration 52000, loss = 0.873532
I0407 14:28:42.501008 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 14:28:42.501026 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 14:28:42.501039 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:28:42.501051 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 14:28:42.501065 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 14:28:42.501085 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.25
I0407 14:28:42.501097 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 14:28:42.501109 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 14:28:42.501121 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 14:28:42.501132 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:28:42.501152 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:28:42.501163 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:28:42.501173 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:28:42.501185 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:28:42.501197 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:28:42.501209 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:28:42.501220 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:28:42.501231 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:28:42.501242 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:28:42.501253 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:28:42.501266 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:28:42.501279 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:28:42.501296 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.76662 (* 0.0454545 = 0.125755 loss)
I0407 14:28:42.501309 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.15516 (* 0.0454545 = 0.143416 loss)
I0407 14:28:42.501323 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.27054 (* 0.0454545 = 0.148661 loss)
I0407 14:28:42.501344 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.02382 (* 0.0454545 = 0.137446 loss)
I0407 14:28:42.501358 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.715 (* 0.0454545 = 0.123409 loss)
I0407 14:28:42.501373 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.56258 (* 0.0454545 = 0.116481 loss)
I0407 14:28:42.501386 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.741756 (* 0.0454545 = 0.0337162 loss)
I0407 14:28:42.501400 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.235163 (* 0.0454545 = 0.0106892 loss)
I0407 14:28:42.501422 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0147441 (* 0.0454545 = 0.000670184 loss)
I0407 14:28:42.501436 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0052231 (* 0.0454545 = 0.000237414 loss)
I0407 14:28:42.501451 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.36877e-05 (* 0.0454545 = 3.80399e-06 loss)
I0407 14:28:42.501464 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 8.54372e-05 (* 0.0454545 = 3.88351e-06 loss)
I0407 14:28:42.501483 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 8.311e-05 (* 0.0454545 = 3.77773e-06 loss)
I0407 14:28:42.501498 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 7.96446e-05 (* 0.0454545 = 3.62021e-06 loss)
I0407 14:28:42.501513 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.61092e-05 (* 0.0454545 = 3.45951e-06 loss)
I0407 14:28:42.501530 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 7.92504e-05 (* 0.0454545 = 3.60229e-06 loss)
I0407 14:28:42.501544 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 8.25867e-05 (* 0.0454545 = 3.75394e-06 loss)
I0407 14:28:42.501581 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 8.49567e-05 (* 0.0454545 = 3.86167e-06 loss)
I0407 14:28:42.501596 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 8.80489e-05 (* 0.0454545 = 4.00223e-06 loss)
I0407 14:28:42.501610 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.87675e-05 (* 0.0454545 = 3.58034e-06 loss)
I0407 14:28:42.501626 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 7.94196e-05 (* 0.0454545 = 3.60998e-06 loss)
I0407 14:28:42.501641 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.74353e-05 (* 0.0454545 = 3.51978e-06 loss)
I0407 14:28:42.501652 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:28:42.501663 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000227295
I0407 14:28:42.501678 32304 sgd_solver.cpp:106] Iteration 52000, lr = 0.00896
I0407 14:29:54.880563 32304 solver.cpp:229] Iteration 52500, loss = 0.878897
I0407 14:29:54.880678 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.28125
I0407 14:29:54.880697 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:29:54.880712 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.1875
I0407 14:29:54.880723 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.21875
I0407 14:29:54.880735 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 14:29:54.880748 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.46875
I0407 14:29:54.880759 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 14:29:54.880770 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 14:29:54.880782 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:29:54.880795 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:29:54.880806 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:29:54.880818 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:29:54.880830 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:29:54.880841 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:29:54.880852 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:29:54.880866 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:29:54.880877 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:29:54.880888 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:29:54.880900 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:29:54.880911 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:29:54.880923 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:29:54.880934 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:29:54.880950 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.67214 (* 0.0454545 = 0.121461 loss)
I0407 14:29:54.880964 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.99879 (* 0.0454545 = 0.136309 loss)
I0407 14:29:54.880978 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.09338 (* 0.0454545 = 0.140608 loss)
I0407 14:29:54.880991 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.97073 (* 0.0454545 = 0.135033 loss)
I0407 14:29:54.881006 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.82167 (* 0.0454545 = 0.128258 loss)
I0407 14:29:54.881019 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.03135 (* 0.0454545 = 0.0923341 loss)
I0407 14:29:54.881032 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.61326 (* 0.0454545 = 0.0733299 loss)
I0407 14:29:54.881047 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.598924 (* 0.0454545 = 0.0272238 loss)
I0407 14:29:54.881060 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.413194 (* 0.0454545 = 0.0187815 loss)
I0407 14:29:54.881078 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00733759 (* 0.0454545 = 0.000333527 loss)
I0407 14:29:54.881093 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 7.16632e-05 (* 0.0454545 = 3.25742e-06 loss)
I0407 14:29:54.881106 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.6013e-05 (* 0.0454545 = 3.45514e-06 loss)
I0407 14:29:54.881120 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.63784e-05 (* 0.0454545 = 3.47175e-06 loss)
I0407 14:29:54.881134 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.48357e-05 (* 0.0454545 = 2.94708e-06 loss)
I0407 14:29:54.881148 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.02195e-05 (* 0.0454545 = 3.1918e-06 loss)
I0407 14:29:54.881162 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.54749e-05 (* 0.0454545 = 2.97613e-06 loss)
I0407 14:29:54.881176 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.8247e-05 (* 0.0454545 = 3.10214e-06 loss)
I0407 14:29:54.881207 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.04131e-05 (* 0.0454545 = 3.2006e-06 loss)
I0407 14:29:54.881223 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.14828e-05 (* 0.0454545 = 3.24922e-06 loss)
I0407 14:29:54.881237 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 6.89437e-05 (* 0.0454545 = 3.13381e-06 loss)
I0407 14:29:54.881250 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.93609e-05 (* 0.0454545 = 3.15277e-06 loss)
I0407 14:29:54.881264 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.35411e-05 (* 0.0454545 = 2.88823e-06 loss)
I0407 14:29:54.881276 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:29:54.881289 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00183497
I0407 14:29:54.881302 32304 sgd_solver.cpp:106] Iteration 52500, lr = 0.00895
I0407 14:31:06.704205 32304 solver.cpp:229] Iteration 53000, loss = 0.872934
I0407 14:31:06.704361 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.21875
I0407 14:31:06.704382 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:31:06.704396 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 14:31:06.704407 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 14:31:06.704419 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:31:06.704432 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5
I0407 14:31:06.704442 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 14:31:06.704454 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 14:31:06.704466 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:31:06.704478 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:31:06.704489 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:31:06.704501 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:31:06.704512 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:31:06.704524 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:31:06.704535 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:31:06.704546 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:31:06.704558 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:31:06.704569 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:31:06.704581 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:31:06.704592 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:31:06.704603 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:31:06.704615 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:31:06.704630 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.62545 (* 0.0454545 = 0.119338 loss)
I0407 14:31:06.704645 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.99151 (* 0.0454545 = 0.135978 loss)
I0407 14:31:06.704659 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.14224 (* 0.0454545 = 0.142829 loss)
I0407 14:31:06.704673 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.11065 (* 0.0454545 = 0.141393 loss)
I0407 14:31:06.704686 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.71512 (* 0.0454545 = 0.123415 loss)
I0407 14:31:06.704699 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.02415 (* 0.0454545 = 0.0920067 loss)
I0407 14:31:06.704713 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.46751 (* 0.0454545 = 0.0667051 loss)
I0407 14:31:06.704726 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.254038 (* 0.0454545 = 0.0115472 loss)
I0407 14:31:06.704741 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.196912 (* 0.0454545 = 0.00895057 loss)
I0407 14:31:06.704754 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00553657 (* 0.0454545 = 0.000251662 loss)
I0407 14:31:06.704769 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.71829e-05 (* 0.0454545 = 2.14468e-06 loss)
I0407 14:31:06.704782 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 5.06204e-05 (* 0.0454545 = 2.30093e-06 loss)
I0407 14:31:06.704797 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.02745e-05 (* 0.0454545 = 2.2852e-06 loss)
I0407 14:31:06.704810 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.82391e-05 (* 0.0454545 = 2.19268e-06 loss)
I0407 14:31:06.704824 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.33488e-05 (* 0.0454545 = 1.9704e-06 loss)
I0407 14:31:06.704838 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.50259e-05 (* 0.0454545 = 2.04663e-06 loss)
I0407 14:31:06.704852 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.86836e-05 (* 0.0454545 = 2.21289e-06 loss)
I0407 14:31:06.704885 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.57693e-05 (* 0.0454545 = 2.08042e-06 loss)
I0407 14:31:06.704900 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 5.13942e-05 (* 0.0454545 = 2.3361e-06 loss)
I0407 14:31:06.704913 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.49249e-05 (* 0.0454545 = 2.04204e-06 loss)
I0407 14:31:06.704931 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.20557e-05 (* 0.0454545 = 1.91162e-06 loss)
I0407 14:31:06.704946 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.8404e-05 (* 0.0454545 = 2.20018e-06 loss)
I0407 14:31:06.704957 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:31:06.704969 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000316954
I0407 14:31:06.704984 32304 sgd_solver.cpp:106] Iteration 53000, lr = 0.00894
I0407 14:32:18.817621 32304 solver.cpp:229] Iteration 53500, loss = 0.873143
I0407 14:32:18.817788 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 14:32:18.817809 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 14:32:18.817822 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:32:18.817834 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 14:32:18.817847 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.09375
I0407 14:32:18.817858 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.21875
I0407 14:32:18.817870 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 14:32:18.817883 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.78125
I0407 14:32:18.817894 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:32:18.817905 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:32:18.817920 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:32:18.817932 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:32:18.817945 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:32:18.817956 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:32:18.817968 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:32:18.817980 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:32:18.817991 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:32:18.818002 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:32:18.818013 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:32:18.818025 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:32:18.818037 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:32:18.818048 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:32:18.818064 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.8972 (* 0.0454545 = 0.131691 loss)
I0407 14:32:18.818078 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.37873 (* 0.0454545 = 0.153579 loss)
I0407 14:32:18.818092 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.48603 (* 0.0454545 = 0.158456 loss)
I0407 14:32:18.818106 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.17825 (* 0.0454545 = 0.144466 loss)
I0407 14:32:18.818120 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.44662 (* 0.0454545 = 0.156664 loss)
I0407 14:32:18.818133 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.9331 (* 0.0454545 = 0.133323 loss)
I0407 14:32:18.818147 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.67786 (* 0.0454545 = 0.0762664 loss)
I0407 14:32:18.818161 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.15493 (* 0.0454545 = 0.0524968 loss)
I0407 14:32:18.818174 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.397822 (* 0.0454545 = 0.0180828 loss)
I0407 14:32:18.818188 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.173771 (* 0.0454545 = 0.00789866 loss)
I0407 14:32:18.818203 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000178795 (* 0.0454545 = 8.12706e-06 loss)
I0407 14:32:18.818217 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000186986 (* 0.0454545 = 8.49936e-06 loss)
I0407 14:32:18.818231 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000192031 (* 0.0454545 = 8.7287e-06 loss)
I0407 14:32:18.818245 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000169755 (* 0.0454545 = 7.71615e-06 loss)
I0407 14:32:18.818261 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000161473 (* 0.0454545 = 7.33967e-06 loss)
I0407 14:32:18.818279 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000166852 (* 0.0454545 = 7.58418e-06 loss)
I0407 14:32:18.818292 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000194203 (* 0.0454545 = 8.82743e-06 loss)
I0407 14:32:18.818320 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000160499 (* 0.0454545 = 7.29542e-06 loss)
I0407 14:32:18.818336 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000183994 (* 0.0454545 = 8.36337e-06 loss)
I0407 14:32:18.818349 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000171487 (* 0.0454545 = 7.79487e-06 loss)
I0407 14:32:18.818363 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000167224 (* 0.0454545 = 7.60107e-06 loss)
I0407 14:32:18.818377 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000156048 (* 0.0454545 = 7.0931e-06 loss)
I0407 14:32:18.818389 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:32:18.818402 32304 solver.cpp:245]     Train net output #45: total_confidence = 3.05702e-06
I0407 14:32:18.818416 32304 sgd_solver.cpp:106] Iteration 53500, lr = 0.00893
I0407 14:33:31.012492 32304 solver.cpp:229] Iteration 54000, loss = 0.869842
I0407 14:33:31.012632 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 14:33:31.012652 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:33:31.012666 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:33:31.012678 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:33:31.012691 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.3125
I0407 14:33:31.012703 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 14:33:31.012714 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 14:33:31.012727 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 14:33:31.012738 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 14:33:31.012750 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:33:31.012761 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:33:31.012773 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:33:31.012784 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:33:31.012795 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:33:31.012809 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:33:31.012820 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:33:31.012831 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:33:31.012842 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:33:31.012855 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:33:31.012866 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:33:31.012876 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:33:31.012888 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:33:31.012904 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.81052 (* 0.0454545 = 0.127751 loss)
I0407 14:33:31.012922 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.23902 (* 0.0454545 = 0.147228 loss)
I0407 14:33:31.012936 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.02982 (* 0.0454545 = 0.137719 loss)
I0407 14:33:31.012950 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.08471 (* 0.0454545 = 0.140214 loss)
I0407 14:33:31.012964 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.8154 (* 0.0454545 = 0.127973 loss)
I0407 14:33:31.012977 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.94295 (* 0.0454545 = 0.13377 loss)
I0407 14:33:31.012990 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.911554 (* 0.0454545 = 0.0414343 loss)
I0407 14:33:31.013005 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.360241 (* 0.0454545 = 0.0163746 loss)
I0407 14:33:31.013018 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0150945 (* 0.0454545 = 0.000686115 loss)
I0407 14:33:31.013032 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00829908 (* 0.0454545 = 0.000377231 loss)
I0407 14:33:31.013046 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.13555e-05 (* 0.0454545 = 3.69798e-06 loss)
I0407 14:33:31.013061 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 8.29867e-05 (* 0.0454545 = 3.77212e-06 loss)
I0407 14:33:31.013074 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 8.08347e-05 (* 0.0454545 = 3.6743e-06 loss)
I0407 14:33:31.013088 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 7.16947e-05 (* 0.0454545 = 3.25885e-06 loss)
I0407 14:33:31.013101 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 8.10338e-05 (* 0.0454545 = 3.68336e-06 loss)
I0407 14:33:31.013115 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 7.33236e-05 (* 0.0454545 = 3.33289e-06 loss)
I0407 14:33:31.013129 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 8.23065e-05 (* 0.0454545 = 3.74121e-06 loss)
I0407 14:33:31.013160 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.94863e-05 (* 0.0454545 = 3.61301e-06 loss)
I0407 14:33:31.013175 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 8.2012e-05 (* 0.0454545 = 3.72782e-06 loss)
I0407 14:33:31.013190 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.17594e-05 (* 0.0454545 = 3.26179e-06 loss)
I0407 14:33:31.013203 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 7.16679e-05 (* 0.0454545 = 3.25763e-06 loss)
I0407 14:33:31.013217 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.35558e-05 (* 0.0454545 = 3.34345e-06 loss)
I0407 14:33:31.013228 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:33:31.013241 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00117817
I0407 14:33:31.013255 32304 sgd_solver.cpp:106] Iteration 54000, lr = 0.00892
I0407 14:34:43.422291 32304 solver.cpp:229] Iteration 54500, loss = 0.870566
I0407 14:34:43.422471 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.21875
I0407 14:34:43.422492 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.15625
I0407 14:34:43.422504 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:34:43.422516 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:34:43.422528 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:34:43.422540 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.21875
I0407 14:34:43.422552 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.5
I0407 14:34:43.422564 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:34:43.422575 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:34:43.422586 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:34:43.422598 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:34:43.422610 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:34:43.422621 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:34:43.422633 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:34:43.422644 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:34:43.422657 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:34:43.422668 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:34:43.422679 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:34:43.422690 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:34:43.422703 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:34:43.422713 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:34:43.422724 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:34:43.422740 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.68851 (* 0.0454545 = 0.122205 loss)
I0407 14:34:43.422755 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.97517 (* 0.0454545 = 0.135235 loss)
I0407 14:34:43.422768 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 2.82668 (* 0.0454545 = 0.128485 loss)
I0407 14:34:43.422782 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.99331 (* 0.0454545 = 0.136059 loss)
I0407 14:34:43.422796 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.05001 (* 0.0454545 = 0.138637 loss)
I0407 14:34:43.422809 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.31536 (* 0.0454545 = 0.105244 loss)
I0407 14:34:43.422823 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.92362 (* 0.0454545 = 0.0874372 loss)
I0407 14:34:43.422837 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.447919 (* 0.0454545 = 0.02036 loss)
I0407 14:34:43.422852 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.119094 (* 0.0454545 = 0.00541337 loss)
I0407 14:34:43.422865 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0149981 (* 0.0454545 = 0.000681731 loss)
I0407 14:34:43.422879 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 6.01193e-05 (* 0.0454545 = 2.73269e-06 loss)
I0407 14:34:43.422894 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 6.46293e-05 (* 0.0454545 = 2.93769e-06 loss)
I0407 14:34:43.422907 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 6.28342e-05 (* 0.0454545 = 2.8561e-06 loss)
I0407 14:34:43.422924 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.22513e-05 (* 0.0454545 = 2.8296e-06 loss)
I0407 14:34:43.422940 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 6.15274e-05 (* 0.0454545 = 2.7967e-06 loss)
I0407 14:34:43.422952 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 5.69303e-05 (* 0.0454545 = 2.58774e-06 loss)
I0407 14:34:43.422966 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 5.52871e-05 (* 0.0454545 = 2.51305e-06 loss)
I0407 14:34:43.423007 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 5.96579e-05 (* 0.0454545 = 2.71172e-06 loss)
I0407 14:34:43.423023 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 5.91844e-05 (* 0.0454545 = 2.6902e-06 loss)
I0407 14:34:43.423038 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 5.49476e-05 (* 0.0454545 = 2.49762e-06 loss)
I0407 14:34:43.423050 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 5.92537e-05 (* 0.0454545 = 2.69335e-06 loss)
I0407 14:34:43.423064 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 5.4057e-05 (* 0.0454545 = 2.45714e-06 loss)
I0407 14:34:43.423084 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:34:43.423095 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000298022
I0407 14:34:43.423110 32304 sgd_solver.cpp:106] Iteration 54500, lr = 0.00891
I0407 14:35:55.451560 32304 solver.cpp:338] Iteration 55000, Testing net (#0)
I0407 14:36:03.511914 32304 solver.cpp:393] Test loss: 0.820077
I0407 14:36:03.511979 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.105
I0407 14:36:03.511996 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.093
I0407 14:36:03.512009 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.089
I0407 14:36:03.512022 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.131
I0407 14:36:03.512033 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.254
I0407 14:36:03.512044 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.498
I0407 14:36:03.512056 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.894
I0407 14:36:03.512068 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 14:36:03.512079 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 14:36:03.512090 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 14:36:03.512102 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 14:36:03.512114 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 14:36:03.512125 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 14:36:03.512136 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 14:36:03.512148 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 14:36:03.512159 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 14:36:03.512171 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 14:36:03.512181 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 14:36:03.512192 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 14:36:03.512203 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 14:36:03.512217 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 14:36:03.512238 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 14:36:03.512265 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 3.30286 (* 0.0454545 = 0.15013 loss)
I0407 14:36:03.512282 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 3.1304 (* 0.0454545 = 0.142291 loss)
I0407 14:36:03.512296 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 3.13818 (* 0.0454545 = 0.142644 loss)
I0407 14:36:03.512310 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 3.04332 (* 0.0454545 = 0.138333 loss)
I0407 14:36:03.512323 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 2.78338 (* 0.0454545 = 0.126517 loss)
I0407 14:36:03.512337 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 1.84542 (* 0.0454545 = 0.0838829 loss)
I0407 14:36:03.512351 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.511843 (* 0.0454545 = 0.0232656 loss)
I0407 14:36:03.512365 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.212933 (* 0.0454545 = 0.00967875 loss)
I0407 14:36:03.512378 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0504156 (* 0.0454545 = 0.00229162 loss)
I0407 14:36:03.512392 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0216209 (* 0.0454545 = 0.000982767 loss)
I0407 14:36:03.512406 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 0.000117181 (* 0.0454545 = 5.32641e-06 loss)
I0407 14:36:03.512420 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 0.00011534 (* 0.0454545 = 5.24271e-06 loss)
I0407 14:36:03.512434 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 0.000108936 (* 0.0454545 = 4.95162e-06 loss)
I0407 14:36:03.512449 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 0.000107941 (* 0.0454545 = 4.90641e-06 loss)
I0407 14:36:03.512462 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 0.000106558 (* 0.0454545 = 4.84354e-06 loss)
I0407 14:36:03.512475 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 0.000108713 (* 0.0454545 = 4.94151e-06 loss)
I0407 14:36:03.512490 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 0.000109896 (* 0.0454545 = 4.99527e-06 loss)
I0407 14:36:03.512550 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 0.000109181 (* 0.0454545 = 4.96278e-06 loss)
I0407 14:36:03.512572 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 0.000111373 (* 0.0454545 = 5.06243e-06 loss)
I0407 14:36:03.512586 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 0.000108403 (* 0.0454545 = 4.9274e-06 loss)
I0407 14:36:03.512600 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 0.000106078 (* 0.0454545 = 4.82174e-06 loss)
I0407 14:36:03.512614 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 0.000103334 (* 0.0454545 = 4.697e-06 loss)
I0407 14:36:03.512626 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0.002
I0407 14:36:03.512637 32304 solver.cpp:406]     Test net output #45: total_confidence = 0.000277449
I0407 14:36:03.547446 32304 solver.cpp:229] Iteration 55000, loss = 0.86821
I0407 14:36:03.547507 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.25
I0407 14:36:03.547523 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:36:03.547535 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:36:03.547549 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 14:36:03.547560 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.3125
I0407 14:36:03.547572 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 14:36:03.547585 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.65625
I0407 14:36:03.547596 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.8125
I0407 14:36:03.547607 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:36:03.547619 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 14:36:03.547631 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:36:03.547642 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:36:03.547654 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:36:03.547667 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:36:03.547677 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:36:03.547689 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:36:03.547700 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:36:03.547711 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:36:03.547724 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:36:03.547734 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:36:03.547745 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:36:03.547757 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:36:03.547772 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.78559 (* 0.0454545 = 0.126618 loss)
I0407 14:36:03.547787 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.31862 (* 0.0454545 = 0.150846 loss)
I0407 14:36:03.547801 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.17065 (* 0.0454545 = 0.14412 loss)
I0407 14:36:03.547814 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.33761 (* 0.0454545 = 0.15171 loss)
I0407 14:36:03.547828 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.82013 (* 0.0454545 = 0.128188 loss)
I0407 14:36:03.547842 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.52697 (* 0.0454545 = 0.114862 loss)
I0407 14:36:03.547855 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.49698 (* 0.0454545 = 0.0680447 loss)
I0407 14:36:03.547869 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.812603 (* 0.0454545 = 0.0369365 loss)
I0407 14:36:03.547883 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.273748 (* 0.0454545 = 0.0124431 loss)
I0407 14:36:03.547922 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.294496 (* 0.0454545 = 0.0133862 loss)
I0407 14:36:03.547937 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 7.44618e-05 (* 0.0454545 = 3.38463e-06 loss)
I0407 14:36:03.547952 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 8.41659e-05 (* 0.0454545 = 3.82572e-06 loss)
I0407 14:36:03.547966 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.13449e-05 (* 0.0454545 = 3.24295e-06 loss)
I0407 14:36:03.547979 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.91534e-05 (* 0.0454545 = 3.14334e-06 loss)
I0407 14:36:03.547993 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.44601e-05 (* 0.0454545 = 3.38455e-06 loss)
I0407 14:36:03.548007 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 7.04438e-05 (* 0.0454545 = 3.20199e-06 loss)
I0407 14:36:03.548022 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.90888e-05 (* 0.0454545 = 3.1404e-06 loss)
I0407 14:36:03.548035 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.56304e-05 (* 0.0454545 = 3.43775e-06 loss)
I0407 14:36:03.548049 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.61199e-05 (* 0.0454545 = 3.46e-06 loss)
I0407 14:36:03.548069 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.149e-05 (* 0.0454545 = 3.24954e-06 loss)
I0407 14:36:03.548102 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.90588e-05 (* 0.0454545 = 3.13904e-06 loss)
I0407 14:36:03.548120 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.94026e-05 (* 0.0454545 = 3.15466e-06 loss)
I0407 14:36:03.548132 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:36:03.548144 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00029282
I0407 14:36:03.548158 32304 sgd_solver.cpp:106] Iteration 55000, lr = 0.0089
I0407 14:37:15.540436 32304 solver.cpp:229] Iteration 55500, loss = 0.870484
I0407 14:37:15.540601 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 14:37:15.540622 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 14:37:15.540637 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.15625
I0407 14:37:15.540648 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:37:15.540660 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 14:37:15.540673 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 14:37:15.540684 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 14:37:15.540696 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 14:37:15.540709 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:37:15.540720 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:37:15.540731 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:37:15.540743 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:37:15.540755 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:37:15.540766 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:37:15.540777 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:37:15.540789 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:37:15.540802 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:37:15.540812 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:37:15.540824 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:37:15.540835 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:37:15.540846 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:37:15.540858 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:37:15.540874 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.62834 (* 0.0454545 = 0.11947 loss)
I0407 14:37:15.540889 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.11588 (* 0.0454545 = 0.141631 loss)
I0407 14:37:15.540902 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 2.89218 (* 0.0454545 = 0.131463 loss)
I0407 14:37:15.540915 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.93634 (* 0.0454545 = 0.13347 loss)
I0407 14:37:15.540932 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.78527 (* 0.0454545 = 0.126603 loss)
I0407 14:37:15.540946 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.41898 (* 0.0454545 = 0.109954 loss)
I0407 14:37:15.540961 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.10937 (* 0.0454545 = 0.0504261 loss)
I0407 14:37:15.540973 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.28788 (* 0.0454545 = 0.0130854 loss)
I0407 14:37:15.540987 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.34145 (* 0.0454545 = 0.0155205 loss)
I0407 14:37:15.541002 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.226643 (* 0.0454545 = 0.0103019 loss)
I0407 14:37:15.541015 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 7.01353e-05 (* 0.0454545 = 3.18797e-06 loss)
I0407 14:37:15.541029 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 6.65353e-05 (* 0.0454545 = 3.02433e-06 loss)
I0407 14:37:15.541043 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 6.37765e-05 (* 0.0454545 = 2.89893e-06 loss)
I0407 14:37:15.541057 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.23401e-05 (* 0.0454545 = 2.83364e-06 loss)
I0407 14:37:15.541082 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 6.50324e-05 (* 0.0454545 = 2.95602e-06 loss)
I0407 14:37:15.541110 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.11142e-05 (* 0.0454545 = 2.77792e-06 loss)
I0407 14:37:15.541138 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.65574e-05 (* 0.0454545 = 3.02534e-06 loss)
I0407 14:37:15.541177 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 6.70663e-05 (* 0.0454545 = 3.04847e-06 loss)
I0407 14:37:15.541194 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.4898e-05 (* 0.0454545 = 2.94991e-06 loss)
I0407 14:37:15.541208 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 6.30017e-05 (* 0.0454545 = 2.86371e-06 loss)
I0407 14:37:15.541223 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.19918e-05 (* 0.0454545 = 2.81781e-06 loss)
I0407 14:37:15.541247 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 5.92895e-05 (* 0.0454545 = 2.69498e-06 loss)
I0407 14:37:15.541270 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:37:15.541285 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000676997
I0407 14:37:15.541299 32304 sgd_solver.cpp:106] Iteration 55500, lr = 0.00889
I0407 14:38:27.762050 32304 solver.cpp:229] Iteration 56000, loss = 0.872649
I0407 14:38:27.762169 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.25
I0407 14:38:27.762189 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:38:27.762202 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:38:27.762215 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.25
I0407 14:38:27.762228 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.46875
I0407 14:38:27.762238 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.71875
I0407 14:38:27.762250 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.90625
I0407 14:38:27.762262 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 14:38:27.762274 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:38:27.762286 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:38:27.762297 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:38:27.762310 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:38:27.762320 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:38:27.762331 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:38:27.762343 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:38:27.762354 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:38:27.762367 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:38:27.762377 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:38:27.762388 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:38:27.762400 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:38:27.762411 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:38:27.762423 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:38:27.762439 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.55513 (* 0.0454545 = 0.116142 loss)
I0407 14:38:27.762452 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.038 (* 0.0454545 = 0.138091 loss)
I0407 14:38:27.762466 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.22268 (* 0.0454545 = 0.146486 loss)
I0407 14:38:27.762480 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.78353 (* 0.0454545 = 0.126524 loss)
I0407 14:38:27.762495 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 1.8828 (* 0.0454545 = 0.085582 loss)
I0407 14:38:27.762508 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.45229 (* 0.0454545 = 0.0660131 loss)
I0407 14:38:27.762521 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.468837 (* 0.0454545 = 0.0213108 loss)
I0407 14:38:27.762537 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.167093 (* 0.0454545 = 0.00759515 loss)
I0407 14:38:27.762550 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.128674 (* 0.0454545 = 0.00584882 loss)
I0407 14:38:27.762564 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.025258 (* 0.0454545 = 0.00114809 loss)
I0407 14:38:27.762578 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000102139 (* 0.0454545 = 4.64268e-06 loss)
I0407 14:38:27.762593 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000105878 (* 0.0454545 = 4.81263e-06 loss)
I0407 14:38:27.762606 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000108798 (* 0.0454545 = 4.94536e-06 loss)
I0407 14:38:27.762619 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000105918 (* 0.0454545 = 4.81447e-06 loss)
I0407 14:38:27.762634 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.00010257 (* 0.0454545 = 4.66227e-06 loss)
I0407 14:38:27.762647 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 9.86137e-05 (* 0.0454545 = 4.48244e-06 loss)
I0407 14:38:27.762661 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000109122 (* 0.0454545 = 4.96011e-06 loss)
I0407 14:38:27.762692 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000118975 (* 0.0454545 = 5.40794e-06 loss)
I0407 14:38:27.762707 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000113182 (* 0.0454545 = 5.14465e-06 loss)
I0407 14:38:27.762722 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000107909 (* 0.0454545 = 4.90497e-06 loss)
I0407 14:38:27.762735 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000100483 (* 0.0454545 = 4.56742e-06 loss)
I0407 14:38:27.762749 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000105751 (* 0.0454545 = 4.80684e-06 loss)
I0407 14:38:27.762760 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:38:27.762773 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000426408
I0407 14:38:27.762787 32304 sgd_solver.cpp:106] Iteration 56000, lr = 0.00888
I0407 14:39:40.254062 32304 solver.cpp:229] Iteration 56500, loss = 0.868728
I0407 14:39:40.254240 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.21875
I0407 14:39:40.254259 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 14:39:40.254273 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:39:40.254286 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.03125
I0407 14:39:40.254297 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 14:39:40.254309 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 14:39:40.254322 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 14:39:40.254333 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:39:40.254344 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:39:40.254356 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:39:40.254369 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:39:40.254379 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:39:40.254391 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:39:40.254402 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:39:40.254415 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:39:40.254426 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:39:40.254437 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:39:40.254448 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:39:40.254459 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:39:40.254470 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:39:40.254482 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:39:40.254493 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:39:40.254509 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.83481 (* 0.0454545 = 0.128855 loss)
I0407 14:39:40.254524 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.2823 (* 0.0454545 = 0.149196 loss)
I0407 14:39:40.254539 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.53712 (* 0.0454545 = 0.160778 loss)
I0407 14:39:40.254551 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.543 (* 0.0454545 = 0.161045 loss)
I0407 14:39:40.254565 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.14797 (* 0.0454545 = 0.14309 loss)
I0407 14:39:40.254580 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.16947 (* 0.0454545 = 0.0986124 loss)
I0407 14:39:40.254593 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.18126 (* 0.0454545 = 0.0536938 loss)
I0407 14:39:40.254606 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.746982 (* 0.0454545 = 0.0339537 loss)
I0407 14:39:40.254621 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.226147 (* 0.0454545 = 0.0102794 loss)
I0407 14:39:40.254634 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0197405 (* 0.0454545 = 0.000897296 loss)
I0407 14:39:40.254649 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 6.38945e-05 (* 0.0454545 = 2.9043e-06 loss)
I0407 14:39:40.254675 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 6.63709e-05 (* 0.0454545 = 3.01686e-06 loss)
I0407 14:39:40.254703 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 6.88365e-05 (* 0.0454545 = 3.12893e-06 loss)
I0407 14:39:40.254719 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.48173e-05 (* 0.0454545 = 2.94624e-06 loss)
I0407 14:39:40.254732 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 6.14858e-05 (* 0.0454545 = 2.79481e-06 loss)
I0407 14:39:40.254747 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.5138e-05 (* 0.0454545 = 2.96082e-06 loss)
I0407 14:39:40.254779 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.57285e-05 (* 0.0454545 = 2.98766e-06 loss)
I0407 14:39:40.254817 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 6.41483e-05 (* 0.0454545 = 2.91583e-06 loss)
I0407 14:39:40.254837 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.61793e-05 (* 0.0454545 = 3.00815e-06 loss)
I0407 14:39:40.254851 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 6.20138e-05 (* 0.0454545 = 2.81881e-06 loss)
I0407 14:39:40.254865 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.34398e-05 (* 0.0454545 = 2.88363e-06 loss)
I0407 14:39:40.254878 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.19352e-05 (* 0.0454545 = 2.81524e-06 loss)
I0407 14:39:40.254897 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:39:40.254909 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000245665
I0407 14:39:40.254927 32304 sgd_solver.cpp:106] Iteration 56500, lr = 0.00887
I0407 14:40:53.671731 32304 solver.cpp:229] Iteration 57000, loss = 0.868981
I0407 14:40:53.671898 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 14:40:53.671921 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:40:53.671936 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:40:53.671947 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.1875
I0407 14:40:53.671959 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 14:40:53.671972 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5
I0407 14:40:53.671983 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.84375
I0407 14:40:53.671994 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 14:40:53.672006 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:40:53.672019 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:40:53.672030 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:40:53.672042 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:40:53.672054 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:40:53.672065 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:40:53.672077 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:40:53.672089 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:40:53.672101 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:40:53.672112 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:40:53.672123 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:40:53.672134 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:40:53.672145 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:40:53.672157 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:40:53.672173 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.08706 (* 0.0454545 = 0.140321 loss)
I0407 14:40:53.672186 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.16958 (* 0.0454545 = 0.144072 loss)
I0407 14:40:53.672204 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.1895 (* 0.0454545 = 0.144977 loss)
I0407 14:40:53.672217 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.02073 (* 0.0454545 = 0.137306 loss)
I0407 14:40:53.672231 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.79223 (* 0.0454545 = 0.126919 loss)
I0407 14:40:53.672245 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.91505 (* 0.0454545 = 0.0870478 loss)
I0407 14:40:53.672260 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.768833 (* 0.0454545 = 0.034947 loss)
I0407 14:40:53.672272 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.242766 (* 0.0454545 = 0.0110348 loss)
I0407 14:40:53.672287 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.139249 (* 0.0454545 = 0.00632948 loss)
I0407 14:40:53.672302 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00707093 (* 0.0454545 = 0.000321406 loss)
I0407 14:40:53.672317 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000135186 (* 0.0454545 = 6.14484e-06 loss)
I0407 14:40:53.672330 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000129401 (* 0.0454545 = 5.88186e-06 loss)
I0407 14:40:53.672343 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000132245 (* 0.0454545 = 6.01113e-06 loss)
I0407 14:40:53.672358 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00012644 (* 0.0454545 = 5.74729e-06 loss)
I0407 14:40:53.672372 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000127369 (* 0.0454545 = 5.7895e-06 loss)
I0407 14:40:53.672386 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000135118 (* 0.0454545 = 6.14173e-06 loss)
I0407 14:40:53.672400 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000147378 (* 0.0454545 = 6.69899e-06 loss)
I0407 14:40:53.672430 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000134168 (* 0.0454545 = 6.09855e-06 loss)
I0407 14:40:53.672444 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000148461 (* 0.0454545 = 6.74825e-06 loss)
I0407 14:40:53.672458 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000137691 (* 0.0454545 = 6.25867e-06 loss)
I0407 14:40:53.672472 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000129994 (* 0.0454545 = 5.90884e-06 loss)
I0407 14:40:53.672485 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000129944 (* 0.0454545 = 5.90654e-06 loss)
I0407 14:40:53.672497 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:40:53.672508 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000414799
I0407 14:40:53.672523 32304 sgd_solver.cpp:106] Iteration 57000, lr = 0.00886
I0407 14:42:06.364665 32304 solver.cpp:229] Iteration 57500, loss = 0.864472
I0407 14:42:06.364864 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.25
I0407 14:42:06.364883 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.15625
I0407 14:42:06.364897 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.15625
I0407 14:42:06.364909 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 14:42:06.364922 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.40625
I0407 14:42:06.364933 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 14:42:06.364945 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 14:42:06.364956 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:42:06.364969 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:42:06.364980 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:42:06.364994 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:42:06.365005 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:42:06.365016 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:42:06.365028 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:42:06.365039 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:42:06.365051 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:42:06.365062 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:42:06.365077 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:42:06.365088 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:42:06.365100 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:42:06.365111 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:42:06.365123 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:42:06.365142 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.36931 (* 0.0454545 = 0.107696 loss)
I0407 14:42:06.365159 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.01448 (* 0.0454545 = 0.137022 loss)
I0407 14:42:06.365172 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 2.91967 (* 0.0454545 = 0.132712 loss)
I0407 14:42:06.365186 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.1787 (* 0.0454545 = 0.144486 loss)
I0407 14:42:06.365200 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.26955 (* 0.0454545 = 0.103161 loss)
I0407 14:42:06.365213 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.98659 (* 0.0454545 = 0.0902994 loss)
I0407 14:42:06.365227 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.736453 (* 0.0454545 = 0.0334751 loss)
I0407 14:42:06.365242 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.397465 (* 0.0454545 = 0.0180666 loss)
I0407 14:42:06.365255 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.111231 (* 0.0454545 = 0.00505597 loss)
I0407 14:42:06.365269 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.183123 (* 0.0454545 = 0.00832378 loss)
I0407 14:42:06.365284 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000212441 (* 0.0454545 = 9.65642e-06 loss)
I0407 14:42:06.365298 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000207651 (* 0.0454545 = 9.43868e-06 loss)
I0407 14:42:06.365313 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000208501 (* 0.0454545 = 9.47731e-06 loss)
I0407 14:42:06.365326 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000216421 (* 0.0454545 = 9.8373e-06 loss)
I0407 14:42:06.365340 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.00021295 (* 0.0454545 = 9.67956e-06 loss)
I0407 14:42:06.365355 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000190536 (* 0.0454545 = 8.66075e-06 loss)
I0407 14:42:06.365368 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000204367 (* 0.0454545 = 9.28942e-06 loss)
I0407 14:42:06.365399 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000218454 (* 0.0454545 = 9.92974e-06 loss)
I0407 14:42:06.365414 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000197334 (* 0.0454545 = 8.96971e-06 loss)
I0407 14:42:06.365428 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000210242 (* 0.0454545 = 9.55645e-06 loss)
I0407 14:42:06.365442 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000194665 (* 0.0454545 = 8.84841e-06 loss)
I0407 14:42:06.365456 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000207563 (* 0.0454545 = 9.43467e-06 loss)
I0407 14:42:06.365468 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0.03125
I0407 14:42:06.365479 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.0013992
I0407 14:42:06.365494 32304 sgd_solver.cpp:106] Iteration 57500, lr = 0.00885
I0407 14:43:19.491720 32304 solver.cpp:229] Iteration 58000, loss = 0.866649
I0407 14:43:19.491869 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.0625
I0407 14:43:19.491890 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 14:43:19.491904 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:43:19.491915 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 14:43:19.491931 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 14:43:19.491945 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 14:43:19.491955 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 14:43:19.491967 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.75
I0407 14:43:19.491979 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.875
I0407 14:43:19.491991 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:43:19.492002 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:43:19.492014 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:43:19.492025 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:43:19.492038 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:43:19.492049 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:43:19.492060 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:43:19.492071 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:43:19.492084 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:43:19.492094 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:43:19.492105 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:43:19.492116 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:43:19.492128 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:43:19.492156 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.94855 (* 0.0454545 = 0.134025 loss)
I0407 14:43:19.492187 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.28226 (* 0.0454545 = 0.149194 loss)
I0407 14:43:19.492204 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.24509 (* 0.0454545 = 0.147504 loss)
I0407 14:43:19.492218 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.24801 (* 0.0454545 = 0.147637 loss)
I0407 14:43:19.492233 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.0413 (* 0.0454545 = 0.138241 loss)
I0407 14:43:19.492246 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.55127 (* 0.0454545 = 0.115967 loss)
I0407 14:43:19.492259 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.27761 (* 0.0454545 = 0.0580732 loss)
I0407 14:43:19.492274 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 1.21122 (* 0.0454545 = 0.0550553 loss)
I0407 14:43:19.492287 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.66946 (* 0.0454545 = 0.03043 loss)
I0407 14:43:19.492300 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.128506 (* 0.0454545 = 0.00584117 loss)
I0407 14:43:19.492316 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 3.19517e-05 (* 0.0454545 = 1.45235e-06 loss)
I0407 14:43:19.492328 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 2.87812e-05 (* 0.0454545 = 1.30824e-06 loss)
I0407 14:43:19.492342 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 2.86807e-05 (* 0.0454545 = 1.30367e-06 loss)
I0407 14:43:19.492357 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 2.68606e-05 (* 0.0454545 = 1.22094e-06 loss)
I0407 14:43:19.492369 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 2.60037e-05 (* 0.0454545 = 1.18199e-06 loss)
I0407 14:43:19.492383 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 2.62367e-05 (* 0.0454545 = 1.19258e-06 loss)
I0407 14:43:19.492398 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 2.8837e-05 (* 0.0454545 = 1.31077e-06 loss)
I0407 14:43:19.492430 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 2.9001e-05 (* 0.0454545 = 1.31823e-06 loss)
I0407 14:43:19.492445 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 3.06739e-05 (* 0.0454545 = 1.39427e-06 loss)
I0407 14:43:19.492460 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 2.79169e-05 (* 0.0454545 = 1.26895e-06 loss)
I0407 14:43:19.492473 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 2.85241e-05 (* 0.0454545 = 1.29655e-06 loss)
I0407 14:43:19.492487 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 2.70898e-05 (* 0.0454545 = 1.23136e-06 loss)
I0407 14:43:19.492499 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:43:19.492511 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000103802
I0407 14:43:19.492525 32304 sgd_solver.cpp:106] Iteration 58000, lr = 0.00884
I0407 14:44:32.698731 32304 solver.cpp:229] Iteration 58500, loss = 0.863644
I0407 14:44:32.698863 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 14:44:32.698884 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:44:32.698897 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:44:32.698909 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 14:44:32.698925 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 14:44:32.698945 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.28125
I0407 14:44:32.698957 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.59375
I0407 14:44:32.698969 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 14:44:32.698982 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.875
I0407 14:44:32.698993 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.90625
I0407 14:44:32.699005 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:44:32.699017 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:44:32.699028 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:44:32.699039 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:44:32.699051 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:44:32.699062 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:44:32.699074 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:44:32.699085 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:44:32.699097 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:44:32.699108 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:44:32.699120 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:44:32.699131 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:44:32.699146 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.54891 (* 0.0454545 = 0.11586 loss)
I0407 14:44:32.699169 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.02305 (* 0.0454545 = 0.137411 loss)
I0407 14:44:32.699183 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.13768 (* 0.0454545 = 0.142622 loss)
I0407 14:44:32.699198 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.11834 (* 0.0454545 = 0.141743 loss)
I0407 14:44:32.699211 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.043 (* 0.0454545 = 0.138318 loss)
I0407 14:44:32.699234 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.72191 (* 0.0454545 = 0.123723 loss)
I0407 14:44:32.699247 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.53244 (* 0.0454545 = 0.0696566 loss)
I0407 14:44:32.699260 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.524983 (* 0.0454545 = 0.0238629 loss)
I0407 14:44:32.699275 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.49497 (* 0.0454545 = 0.0224986 loss)
I0407 14:44:32.699288 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.452364 (* 0.0454545 = 0.020562 loss)
I0407 14:44:32.699303 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.589e-05 (* 0.0454545 = 3.90409e-06 loss)
I0407 14:44:32.699336 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.45212e-05 (* 0.0454545 = 3.38733e-06 loss)
I0407 14:44:32.699353 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.88665e-05 (* 0.0454545 = 3.58484e-06 loss)
I0407 14:44:32.699368 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.1438e-05 (* 0.0454545 = 3.70173e-06 loss)
I0407 14:44:32.699383 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.18026e-05 (* 0.0454545 = 3.26376e-06 loss)
I0407 14:44:32.699396 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 7.25511e-05 (* 0.0454545 = 3.29778e-06 loss)
I0407 14:44:32.699424 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 7.70812e-05 (* 0.0454545 = 3.50369e-06 loss)
I0407 14:44:32.699461 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.93807e-05 (* 0.0454545 = 3.60821e-06 loss)
I0407 14:44:32.699476 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.39626e-05 (* 0.0454545 = 3.36194e-06 loss)
I0407 14:44:32.699491 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.80406e-05 (* 0.0454545 = 3.5473e-06 loss)
I0407 14:44:32.699506 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 7.11862e-05 (* 0.0454545 = 3.23574e-06 loss)
I0407 14:44:32.699519 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.0604e-05 (* 0.0454545 = 3.20927e-06 loss)
I0407 14:44:32.699532 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:44:32.699542 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000234808
I0407 14:44:32.699556 32304 sgd_solver.cpp:106] Iteration 58500, lr = 0.00883
I0407 14:45:43.604168 32304 solver.cpp:229] Iteration 59000, loss = 0.867253
I0407 14:45:43.604280 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.28125
I0407 14:45:43.604298 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:45:43.604311 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:45:43.604323 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:45:43.604336 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 14:45:43.604348 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 14:45:43.604360 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 14:45:43.604372 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 14:45:43.604383 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 14:45:43.604395 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:45:43.604406 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:45:43.604418 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:45:43.604429 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:45:43.604441 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:45:43.604452 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:45:43.604465 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:45:43.604475 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:45:43.604487 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:45:43.604498 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:45:43.604509 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:45:43.604521 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:45:43.604532 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:45:43.604548 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.59628 (* 0.0454545 = 0.118013 loss)
I0407 14:45:43.604562 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.29057 (* 0.0454545 = 0.149571 loss)
I0407 14:45:43.604576 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.1016 (* 0.0454545 = 0.140982 loss)
I0407 14:45:43.604589 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.01723 (* 0.0454545 = 0.137147 loss)
I0407 14:45:43.604604 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.73797 (* 0.0454545 = 0.124453 loss)
I0407 14:45:43.604616 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.48988 (* 0.0454545 = 0.113176 loss)
I0407 14:45:43.604630 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.0857 (* 0.0454545 = 0.04935 loss)
I0407 14:45:43.604643 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.225972 (* 0.0454545 = 0.0102714 loss)
I0407 14:45:43.604657 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.417936 (* 0.0454545 = 0.0189971 loss)
I0407 14:45:43.604671 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.227451 (* 0.0454545 = 0.0103387 loss)
I0407 14:45:43.604686 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.08409e-05 (* 0.0454545 = 1.85641e-06 loss)
I0407 14:45:43.604699 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.26985e-05 (* 0.0454545 = 1.94084e-06 loss)
I0407 14:45:43.604713 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.26839e-05 (* 0.0454545 = 1.94018e-06 loss)
I0407 14:45:43.604727 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.47389e-05 (* 0.0454545 = 2.03359e-06 loss)
I0407 14:45:43.604742 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.98891e-05 (* 0.0454545 = 1.81314e-06 loss)
I0407 14:45:43.604754 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.88385e-05 (* 0.0454545 = 1.76539e-06 loss)
I0407 14:45:43.604768 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.38522e-05 (* 0.0454545 = 1.99328e-06 loss)
I0407 14:45:43.604799 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.17929e-05 (* 0.0454545 = 1.89968e-06 loss)
I0407 14:45:43.604815 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.44442e-05 (* 0.0454545 = 2.02019e-06 loss)
I0407 14:45:43.604828 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.38278e-05 (* 0.0454545 = 1.99217e-06 loss)
I0407 14:45:43.604841 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.34533e-05 (* 0.0454545 = 1.97515e-06 loss)
I0407 14:45:43.604856 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.2047e-05 (* 0.0454545 = 1.91123e-06 loss)
I0407 14:45:43.604867 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:45:43.604879 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000875343
I0407 14:45:43.604892 32304 sgd_solver.cpp:106] Iteration 59000, lr = 0.00882
I0407 14:46:56.781116 32304 solver.cpp:229] Iteration 59500, loss = 0.859938
I0407 14:46:56.781224 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.15625
I0407 14:46:56.781242 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 14:46:56.781256 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:46:56.781268 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:46:56.781281 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.3125
I0407 14:46:56.781293 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 14:46:56.781304 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 14:46:56.781316 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 14:46:56.781328 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:46:56.781339 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:46:56.781353 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:46:56.781364 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:46:56.781375 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:46:56.781388 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:46:56.781399 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:46:56.781409 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:46:56.781421 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:46:56.781432 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:46:56.781443 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:46:56.781455 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:46:56.781466 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:46:56.781477 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:46:56.781493 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.80234 (* 0.0454545 = 0.127379 loss)
I0407 14:46:56.781508 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.12299 (* 0.0454545 = 0.141954 loss)
I0407 14:46:56.781522 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.11705 (* 0.0454545 = 0.141684 loss)
I0407 14:46:56.781535 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.02741 (* 0.0454545 = 0.13761 loss)
I0407 14:46:56.781549 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.75255 (* 0.0454545 = 0.125116 loss)
I0407 14:46:56.781563 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.31926 (* 0.0454545 = 0.105421 loss)
I0407 14:46:56.781577 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.84167 (* 0.0454545 = 0.0382577 loss)
I0407 14:46:56.781590 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.267765 (* 0.0454545 = 0.0121711 loss)
I0407 14:46:56.781605 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.137877 (* 0.0454545 = 0.00626713 loss)
I0407 14:46:56.781620 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0225684 (* 0.0454545 = 0.00102584 loss)
I0407 14:46:56.781633 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.13264e-05 (* 0.0454545 = 1.87847e-06 loss)
I0407 14:46:56.781648 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.86111e-05 (* 0.0454545 = 2.2096e-06 loss)
I0407 14:46:56.781662 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 3.91654e-05 (* 0.0454545 = 1.78025e-06 loss)
I0407 14:46:56.781677 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 3.90424e-05 (* 0.0454545 = 1.77465e-06 loss)
I0407 14:46:56.781692 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.79454e-05 (* 0.0454545 = 1.72479e-06 loss)
I0407 14:46:56.781704 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.51395e-05 (* 0.0454545 = 1.59725e-06 loss)
I0407 14:46:56.781718 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 3.89975e-05 (* 0.0454545 = 1.77261e-06 loss)
I0407 14:46:56.781749 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.10952e-05 (* 0.0454545 = 1.86797e-06 loss)
I0407 14:46:56.781764 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.38436e-05 (* 0.0454545 = 1.99289e-06 loss)
I0407 14:46:56.781781 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.09427e-05 (* 0.0454545 = 1.86103e-06 loss)
I0407 14:46:56.781811 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 3.68478e-05 (* 0.0454545 = 1.6749e-06 loss)
I0407 14:46:56.781837 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.84409e-05 (* 0.0454545 = 1.74732e-06 loss)
I0407 14:46:56.781850 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:46:56.781862 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000720828
I0407 14:46:56.781875 32304 sgd_solver.cpp:106] Iteration 59500, lr = 0.00881
I0407 14:48:09.154619 32304 solver.cpp:338] Iteration 60000, Testing net (#0)
I0407 14:48:17.112709 32304 solver.cpp:393] Test loss: 0.74853
I0407 14:48:17.112756 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.199
I0407 14:48:17.112771 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.092
I0407 14:48:17.112784 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.099
I0407 14:48:17.112797 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.148
I0407 14:48:17.112807 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.246
I0407 14:48:17.112819 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.52
I0407 14:48:17.112831 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.894
I0407 14:48:17.112843 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 14:48:17.112854 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 14:48:17.112865 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 14:48:17.112877 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 14:48:17.112889 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 14:48:17.112900 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 14:48:17.112911 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 14:48:17.112926 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 14:48:17.112938 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 14:48:17.112949 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 14:48:17.112960 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 14:48:17.112972 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 14:48:17.112982 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 14:48:17.112993 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 14:48:17.113004 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 14:48:17.113018 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 2.81672 (* 0.0454545 = 0.128033 loss)
I0407 14:48:17.113034 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 2.88041 (* 0.0454545 = 0.130928 loss)
I0407 14:48:17.113046 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 2.9132 (* 0.0454545 = 0.132418 loss)
I0407 14:48:17.113060 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 2.81634 (* 0.0454545 = 0.128016 loss)
I0407 14:48:17.113073 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 2.59295 (* 0.0454545 = 0.117861 loss)
I0407 14:48:17.113086 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 1.7304 (* 0.0454545 = 0.0786547 loss)
I0407 14:48:17.113100 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.440687 (* 0.0454545 = 0.0200312 loss)
I0407 14:48:17.113113 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.21469 (* 0.0454545 = 0.00975863 loss)
I0407 14:48:17.113127 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0425814 (* 0.0454545 = 0.00193552 loss)
I0407 14:48:17.113142 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0186495 (* 0.0454545 = 0.000847706 loss)
I0407 14:48:17.113154 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 8.45839e-05 (* 0.0454545 = 3.84472e-06 loss)
I0407 14:48:17.113168 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 9.04404e-05 (* 0.0454545 = 4.11093e-06 loss)
I0407 14:48:17.113183 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 9.01121e-05 (* 0.0454545 = 4.09601e-06 loss)
I0407 14:48:17.113196 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 8.49847e-05 (* 0.0454545 = 3.86294e-06 loss)
I0407 14:48:17.113210 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 8.19552e-05 (* 0.0454545 = 3.72524e-06 loss)
I0407 14:48:17.113224 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 9.03224e-05 (* 0.0454545 = 4.10556e-06 loss)
I0407 14:48:17.113239 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 8.57735e-05 (* 0.0454545 = 3.89879e-06 loss)
I0407 14:48:17.113287 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 8.57895e-05 (* 0.0454545 = 3.89952e-06 loss)
I0407 14:48:17.113302 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 8.88069e-05 (* 0.0454545 = 4.03668e-06 loss)
I0407 14:48:17.113317 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 8.6381e-05 (* 0.0454545 = 3.92641e-06 loss)
I0407 14:48:17.113330 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 8.23916e-05 (* 0.0454545 = 3.74507e-06 loss)
I0407 14:48:17.113343 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 8.52659e-05 (* 0.0454545 = 3.87572e-06 loss)
I0407 14:48:17.113355 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0
I0407 14:48:17.113366 32304 solver.cpp:406]     Test net output #45: total_confidence = 0.000479688
I0407 14:48:17.147706 32304 solver.cpp:229] Iteration 60000, loss = 0.863005
I0407 14:48:17.147748 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.25
I0407 14:48:17.147765 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.21875
I0407 14:48:17.147778 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 14:48:17.147790 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.1875
I0407 14:48:17.147804 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.375
I0407 14:48:17.147814 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5
I0407 14:48:17.147826 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 14:48:17.147837 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:48:17.147850 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:48:17.147861 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:48:17.147872 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:48:17.147884 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:48:17.147896 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:48:17.147907 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:48:17.147918 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:48:17.147929 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:48:17.147940 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:48:17.147951 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:48:17.147963 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:48:17.147974 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:48:17.147985 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:48:17.147996 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:48:17.148010 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.66949 (* 0.0454545 = 0.12134 loss)
I0407 14:48:17.148025 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.01477 (* 0.0454545 = 0.137035 loss)
I0407 14:48:17.148038 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.0328 (* 0.0454545 = 0.137855 loss)
I0407 14:48:17.148052 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.07428 (* 0.0454545 = 0.13974 loss)
I0407 14:48:17.148066 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.55311 (* 0.0454545 = 0.11605 loss)
I0407 14:48:17.148082 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.91535 (* 0.0454545 = 0.0870616 loss)
I0407 14:48:17.148097 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.67837 (* 0.0454545 = 0.030835 loss)
I0407 14:48:17.148110 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.366902 (* 0.0454545 = 0.0166773 loss)
I0407 14:48:17.148124 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.137263 (* 0.0454545 = 0.00623921 loss)
I0407 14:48:17.148138 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.170393 (* 0.0454545 = 0.00774514 loss)
I0407 14:48:17.148172 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 4.98467e-05 (* 0.0454545 = 2.26576e-06 loss)
I0407 14:48:17.148187 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 4.82257e-05 (* 0.0454545 = 2.19208e-06 loss)
I0407 14:48:17.148201 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.94518e-05 (* 0.0454545 = 2.24781e-06 loss)
I0407 14:48:17.148216 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.34175e-05 (* 0.0454545 = 1.97352e-06 loss)
I0407 14:48:17.148229 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.12417e-05 (* 0.0454545 = 1.87462e-06 loss)
I0407 14:48:17.148243 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.59329e-05 (* 0.0454545 = 2.08786e-06 loss)
I0407 14:48:17.148257 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.2818e-05 (* 0.0454545 = 1.94627e-06 loss)
I0407 14:48:17.148270 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.89282e-05 (* 0.0454545 = 2.22401e-06 loss)
I0407 14:48:17.148284 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.77715e-05 (* 0.0454545 = 2.17143e-06 loss)
I0407 14:48:17.148298 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.51129e-05 (* 0.0454545 = 2.05059e-06 loss)
I0407 14:48:17.148311 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.74957e-05 (* 0.0454545 = 2.1589e-06 loss)
I0407 14:48:17.148325 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.95616e-05 (* 0.0454545 = 1.79825e-06 loss)
I0407 14:48:17.148337 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:48:17.148350 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000362798
I0407 14:48:17.148363 32304 sgd_solver.cpp:106] Iteration 60000, lr = 0.0088
I0407 14:49:28.047634 32304 solver.cpp:229] Iteration 60500, loss = 0.861302
I0407 14:49:28.047794 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.3125
I0407 14:49:28.047814 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 14:49:28.047827 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 14:49:28.047839 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:49:28.047852 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 14:49:28.047863 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 14:49:28.047875 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 14:49:28.047888 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 14:49:28.047899 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 14:49:28.047910 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.9375
I0407 14:49:28.047925 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:49:28.047937 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:49:28.047950 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:49:28.047960 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:49:28.047971 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:49:28.047982 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:49:28.047993 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:49:28.048005 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:49:28.048017 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:49:28.048027 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:49:28.048038 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:49:28.048050 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:49:28.048065 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.96508 (* 0.0454545 = 0.134776 loss)
I0407 14:49:28.048080 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.25624 (* 0.0454545 = 0.148011 loss)
I0407 14:49:28.048094 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.48649 (* 0.0454545 = 0.158477 loss)
I0407 14:49:28.048107 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.46062 (* 0.0454545 = 0.157301 loss)
I0407 14:49:28.048121 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.03567 (* 0.0454545 = 0.137985 loss)
I0407 14:49:28.048135 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.18647 (* 0.0454545 = 0.0993848 loss)
I0407 14:49:28.048149 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.29567 (* 0.0454545 = 0.0588941 loss)
I0407 14:49:28.048163 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.700693 (* 0.0454545 = 0.0318497 loss)
I0407 14:49:28.048177 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.655561 (* 0.0454545 = 0.0297982 loss)
I0407 14:49:28.048190 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.37089 (* 0.0454545 = 0.0168586 loss)
I0407 14:49:28.048205 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 7.2047e-05 (* 0.0454545 = 3.27486e-06 loss)
I0407 14:49:28.048219 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.56412e-05 (* 0.0454545 = 3.43824e-06 loss)
I0407 14:49:28.048233 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.77615e-05 (* 0.0454545 = 3.53461e-06 loss)
I0407 14:49:28.048248 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 7.09596e-05 (* 0.0454545 = 3.22543e-06 loss)
I0407 14:49:28.048260 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.05478e-05 (* 0.0454545 = 3.20672e-06 loss)
I0407 14:49:28.048274 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 7.32876e-05 (* 0.0454545 = 3.33125e-06 loss)
I0407 14:49:28.048288 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 7.42506e-05 (* 0.0454545 = 3.37503e-06 loss)
I0407 14:49:28.048316 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.1906e-05 (* 0.0454545 = 3.26845e-06 loss)
I0407 14:49:28.048331 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.44379e-05 (* 0.0454545 = 3.38354e-06 loss)
I0407 14:49:28.048344 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.00515e-05 (* 0.0454545 = 3.18416e-06 loss)
I0407 14:49:28.048362 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.8362e-05 (* 0.0454545 = 3.10737e-06 loss)
I0407 14:49:28.048377 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.32309e-05 (* 0.0454545 = 3.32868e-06 loss)
I0407 14:49:28.048389 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:49:28.048400 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000149204
I0407 14:49:28.048413 32304 sgd_solver.cpp:106] Iteration 60500, lr = 0.00879
I0407 14:50:40.768414 32304 solver.cpp:229] Iteration 61000, loss = 0.858431
I0407 14:50:40.768546 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.28125
I0407 14:50:40.768566 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 14:50:40.768579 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:50:40.768591 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:50:40.768604 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.3125
I0407 14:50:40.768615 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.46875
I0407 14:50:40.768626 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.90625
I0407 14:50:40.768638 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.96875
I0407 14:50:40.768651 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 14:50:40.768662 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:50:40.768673 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:50:40.768685 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:50:40.768697 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:50:40.768708 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:50:40.768720 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:50:40.768731 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:50:40.768743 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:50:40.768754 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:50:40.768765 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:50:40.768776 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:50:40.768789 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:50:40.768800 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:50:40.768816 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.68302 (* 0.0454545 = 0.121956 loss)
I0407 14:50:40.768831 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.11159 (* 0.0454545 = 0.141436 loss)
I0407 14:50:40.768844 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.05992 (* 0.0454545 = 0.139087 loss)
I0407 14:50:40.768857 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.0002 (* 0.0454545 = 0.136373 loss)
I0407 14:50:40.768872 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.57657 (* 0.0454545 = 0.117117 loss)
I0407 14:50:40.768884 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.90042 (* 0.0454545 = 0.0863829 loss)
I0407 14:50:40.768898 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.483786 (* 0.0454545 = 0.0219903 loss)
I0407 14:50:40.768911 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.175446 (* 0.0454545 = 0.00797481 loss)
I0407 14:50:40.768929 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0128552 (* 0.0454545 = 0.000584327 loss)
I0407 14:50:40.768944 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00506674 (* 0.0454545 = 0.000230306 loss)
I0407 14:50:40.768957 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000163378 (* 0.0454545 = 7.42626e-06 loss)
I0407 14:50:40.768971 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000159572 (* 0.0454545 = 7.25328e-06 loss)
I0407 14:50:40.768985 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000154866 (* 0.0454545 = 7.03938e-06 loss)
I0407 14:50:40.768998 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000163536 (* 0.0454545 = 7.43343e-06 loss)
I0407 14:50:40.769012 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000176629 (* 0.0454545 = 8.0286e-06 loss)
I0407 14:50:40.769026 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000148057 (* 0.0454545 = 6.72987e-06 loss)
I0407 14:50:40.769040 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000146983 (* 0.0454545 = 6.68106e-06 loss)
I0407 14:50:40.769073 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000169465 (* 0.0454545 = 7.70297e-06 loss)
I0407 14:50:40.769088 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000155778 (* 0.0454545 = 7.0808e-06 loss)
I0407 14:50:40.769101 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000161259 (* 0.0454545 = 7.32996e-06 loss)
I0407 14:50:40.769115 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000159061 (* 0.0454545 = 7.23005e-06 loss)
I0407 14:50:40.769129 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000152868 (* 0.0454545 = 6.94853e-06 loss)
I0407 14:50:40.769140 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:50:40.769152 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000501369
I0407 14:50:40.769166 32304 sgd_solver.cpp:106] Iteration 61000, lr = 0.00878
I0407 14:51:53.186125 32304 solver.cpp:229] Iteration 61500, loss = 0.858871
I0407 14:51:53.186311 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.09375
I0407 14:51:53.186331 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:51:53.186343 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.1875
I0407 14:51:53.186355 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0
I0407 14:51:53.186367 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 14:51:53.186379 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 14:51:53.186390 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 14:51:53.186403 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:51:53.186414 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 14:51:53.186427 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.90625
I0407 14:51:53.186439 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:51:53.186450 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:51:53.186462 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:51:53.186475 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:51:53.186497 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:51:53.186517 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:51:53.186532 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:51:53.186542 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:51:53.186554 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:51:53.186565 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:51:53.186578 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:51:53.186589 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:51:53.186604 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.69294 (* 0.0454545 = 0.122407 loss)
I0407 14:51:53.186622 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.04741 (* 0.0454545 = 0.138519 loss)
I0407 14:51:53.186636 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 2.97647 (* 0.0454545 = 0.135294 loss)
I0407 14:51:53.186650 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.10074 (* 0.0454545 = 0.140943 loss)
I0407 14:51:53.186663 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.84449 (* 0.0454545 = 0.129295 loss)
I0407 14:51:53.186677 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.00874 (* 0.0454545 = 0.0913065 loss)
I0407 14:51:53.186691 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.03411 (* 0.0454545 = 0.0470049 loss)
I0407 14:51:53.186704 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.399174 (* 0.0454545 = 0.0181443 loss)
I0407 14:51:53.186718 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.360443 (* 0.0454545 = 0.0163838 loss)
I0407 14:51:53.186733 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.380572 (* 0.0454545 = 0.0172987 loss)
I0407 14:51:53.186746 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 9.58436e-05 (* 0.0454545 = 4.35653e-06 loss)
I0407 14:51:53.186760 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000101504 (* 0.0454545 = 4.6138e-06 loss)
I0407 14:51:53.186774 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000100272 (* 0.0454545 = 4.5578e-06 loss)
I0407 14:51:53.186789 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.63404e-05 (* 0.0454545 = 3.92457e-06 loss)
I0407 14:51:53.186803 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 8.91475e-05 (* 0.0454545 = 4.05216e-06 loss)
I0407 14:51:53.186816 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 9.74129e-05 (* 0.0454545 = 4.42786e-06 loss)
I0407 14:51:53.186830 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 9.52811e-05 (* 0.0454545 = 4.33096e-06 loss)
I0407 14:51:53.186861 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 8.84055e-05 (* 0.0454545 = 4.01843e-06 loss)
I0407 14:51:53.186877 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 9.96459e-05 (* 0.0454545 = 4.52936e-06 loss)
I0407 14:51:53.186890 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 9.41331e-05 (* 0.0454545 = 4.27878e-06 loss)
I0407 14:51:53.186903 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 9.8226e-05 (* 0.0454545 = 4.46482e-06 loss)
I0407 14:51:53.186918 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 8.75581e-05 (* 0.0454545 = 3.97991e-06 loss)
I0407 14:51:53.186930 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:51:53.186941 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000396696
I0407 14:51:53.186956 32304 sgd_solver.cpp:106] Iteration 61500, lr = 0.00877
I0407 14:53:05.595129 32304 solver.cpp:229] Iteration 62000, loss = 0.859207
I0407 14:53:05.595271 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.34375
I0407 14:53:05.595293 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 14:53:05.595305 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:53:05.595336 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:53:05.595352 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 14:53:05.595366 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 14:53:05.595377 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.59375
I0407 14:53:05.595388 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.8125
I0407 14:53:05.595401 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:53:05.595412 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 14:53:05.595424 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:53:05.595437 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:53:05.595448 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:53:05.595459 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:53:05.595470 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:53:05.595481 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:53:05.595494 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:53:05.595504 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:53:05.595515 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:53:05.595526 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:53:05.595537 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:53:05.595548 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:53:05.595564 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.4504 (* 0.0454545 = 0.111382 loss)
I0407 14:53:05.595578 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.96462 (* 0.0454545 = 0.134755 loss)
I0407 14:53:05.595592 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 2.85905 (* 0.0454545 = 0.129957 loss)
I0407 14:53:05.595607 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.98142 (* 0.0454545 = 0.135519 loss)
I0407 14:53:05.595620 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.96161 (* 0.0454545 = 0.134619 loss)
I0407 14:53:05.595634 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.2989 (* 0.0454545 = 0.104496 loss)
I0407 14:53:05.595648 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.60671 (* 0.0454545 = 0.0730322 loss)
I0407 14:53:05.595661 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.655254 (* 0.0454545 = 0.0297843 loss)
I0407 14:53:05.595674 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.232507 (* 0.0454545 = 0.0105685 loss)
I0407 14:53:05.595688 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.160955 (* 0.0454545 = 0.00731615 loss)
I0407 14:53:05.595702 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 7.36724e-05 (* 0.0454545 = 3.34874e-06 loss)
I0407 14:53:05.595717 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.18781e-05 (* 0.0454545 = 3.26718e-06 loss)
I0407 14:53:05.595731 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.46493e-05 (* 0.0454545 = 3.39315e-06 loss)
I0407 14:53:05.595744 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 7.29685e-05 (* 0.0454545 = 3.31675e-06 loss)
I0407 14:53:05.595758 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 6.08953e-05 (* 0.0454545 = 2.76797e-06 loss)
I0407 14:53:05.595772 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.22531e-05 (* 0.0454545 = 2.82969e-06 loss)
I0407 14:53:05.595785 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 7.13273e-05 (* 0.0454545 = 3.24215e-06 loss)
I0407 14:53:05.595813 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.34039e-05 (* 0.0454545 = 3.33654e-06 loss)
I0407 14:53:05.595829 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.81685e-05 (* 0.0454545 = 3.09857e-06 loss)
I0407 14:53:05.595841 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.22651e-05 (* 0.0454545 = 3.28478e-06 loss)
I0407 14:53:05.595855 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 7.26799e-05 (* 0.0454545 = 3.30363e-06 loss)
I0407 14:53:05.595870 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.12491e-05 (* 0.0454545 = 3.23859e-06 loss)
I0407 14:53:05.595881 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:53:05.595892 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000314031
I0407 14:53:05.595906 32304 sgd_solver.cpp:106] Iteration 62000, lr = 0.00876
I0407 14:54:18.134879 32304 solver.cpp:229] Iteration 62500, loss = 0.856314
I0407 14:54:18.134999 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.3125
I0407 14:54:18.135017 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.21875
I0407 14:54:18.135030 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:54:18.135042 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:54:18.135054 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.1875
I0407 14:54:18.135066 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 14:54:18.135078 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 14:54:18.135090 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.8125
I0407 14:54:18.135102 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:54:18.135114 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:54:18.135125 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:54:18.135138 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:54:18.135149 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:54:18.135160 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:54:18.135171 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:54:18.135182 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:54:18.135195 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:54:18.135205 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:54:18.135216 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:54:18.135227 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:54:18.135239 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:54:18.135251 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:54:18.135265 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.56588 (* 0.0454545 = 0.116631 loss)
I0407 14:54:18.135283 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.8717 (* 0.0454545 = 0.130532 loss)
I0407 14:54:18.135310 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 2.91493 (* 0.0454545 = 0.132497 loss)
I0407 14:54:18.135346 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.90737 (* 0.0454545 = 0.132153 loss)
I0407 14:54:18.135362 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.73156 (* 0.0454545 = 0.124162 loss)
I0407 14:54:18.135376 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.5901 (* 0.0454545 = 0.117732 loss)
I0407 14:54:18.135390 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.65157 (* 0.0454545 = 0.0750712 loss)
I0407 14:54:18.135403 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.779767 (* 0.0454545 = 0.0354439 loss)
I0407 14:54:18.135417 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.366575 (* 0.0454545 = 0.0166625 loss)
I0407 14:54:18.135431 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.056969 (* 0.0454545 = 0.0025895 loss)
I0407 14:54:18.135444 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000114543 (* 0.0454545 = 5.20651e-06 loss)
I0407 14:54:18.135459 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000119666 (* 0.0454545 = 5.43935e-06 loss)
I0407 14:54:18.135473 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000106273 (* 0.0454545 = 4.83059e-06 loss)
I0407 14:54:18.135486 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000109436 (* 0.0454545 = 4.97434e-06 loss)
I0407 14:54:18.135500 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000102426 (* 0.0454545 = 4.65573e-06 loss)
I0407 14:54:18.135514 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 9.70727e-05 (* 0.0454545 = 4.4124e-06 loss)
I0407 14:54:18.135529 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000113519 (* 0.0454545 = 5.15995e-06 loss)
I0407 14:54:18.135560 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000115163 (* 0.0454545 = 5.23469e-06 loss)
I0407 14:54:18.135576 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000109355 (* 0.0454545 = 4.97067e-06 loss)
I0407 14:54:18.135591 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000117042 (* 0.0454545 = 5.32007e-06 loss)
I0407 14:54:18.135603 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000111184 (* 0.0454545 = 5.05382e-06 loss)
I0407 14:54:18.135617 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000107787 (* 0.0454545 = 4.8994e-06 loss)
I0407 14:54:18.135629 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:54:18.135640 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00139227
I0407 14:54:18.135653 32304 sgd_solver.cpp:106] Iteration 62500, lr = 0.00875
I0407 14:55:30.278053 32304 solver.cpp:229] Iteration 63000, loss = 0.852652
I0407 14:55:30.278195 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.25
I0407 14:55:30.278216 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:55:30.278230 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 14:55:30.278242 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.21875
I0407 14:55:30.278254 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.375
I0407 14:55:30.278266 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.46875
I0407 14:55:30.278278 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.6875
I0407 14:55:30.278290 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 1
I0407 14:55:30.278302 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 14:55:30.278313 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:55:30.278324 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:55:30.278337 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:55:30.278347 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:55:30.278358 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:55:30.278369 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:55:30.278381 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:55:30.278393 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:55:30.278403 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:55:30.278414 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:55:30.278425 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:55:30.278437 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:55:30.278448 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:55:30.278465 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.7597 (* 0.0454545 = 0.125441 loss)
I0407 14:55:30.278478 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.18685 (* 0.0454545 = 0.144857 loss)
I0407 14:55:30.278492 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.08609 (* 0.0454545 = 0.140277 loss)
I0407 14:55:30.278506 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.94834 (* 0.0454545 = 0.134016 loss)
I0407 14:55:30.278519 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.41446 (* 0.0454545 = 0.109748 loss)
I0407 14:55:30.278533 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.90147 (* 0.0454545 = 0.0864305 loss)
I0407 14:55:30.278548 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.26022 (* 0.0454545 = 0.0572827 loss)
I0407 14:55:30.278561 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.0324601 (* 0.0454545 = 0.00147546 loss)
I0407 14:55:30.278575 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.00560025 (* 0.0454545 = 0.000254557 loss)
I0407 14:55:30.278590 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00149713 (* 0.0454545 = 6.80515e-05 loss)
I0407 14:55:30.278604 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 5.02297e-05 (* 0.0454545 = 2.28317e-06 loss)
I0407 14:55:30.278619 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 5.14533e-05 (* 0.0454545 = 2.33879e-06 loss)
I0407 14:55:30.278631 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 4.28379e-05 (* 0.0454545 = 1.94718e-06 loss)
I0407 14:55:30.278645 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 4.8027e-05 (* 0.0454545 = 2.18305e-06 loss)
I0407 14:55:30.278659 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.40366e-05 (* 0.0454545 = 2.00167e-06 loss)
I0407 14:55:30.278673 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.16351e-05 (* 0.0454545 = 1.8925e-06 loss)
I0407 14:55:30.278687 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.59478e-05 (* 0.0454545 = 2.08854e-06 loss)
I0407 14:55:30.278718 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 5.37153e-05 (* 0.0454545 = 2.4416e-06 loss)
I0407 14:55:30.278733 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.95628e-05 (* 0.0454545 = 2.25286e-06 loss)
I0407 14:55:30.278748 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 4.8576e-05 (* 0.0454545 = 2.208e-06 loss)
I0407 14:55:30.278761 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.69818e-05 (* 0.0454545 = 2.13554e-06 loss)
I0407 14:55:30.278775 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 5.04104e-05 (* 0.0454545 = 2.29138e-06 loss)
I0407 14:55:30.278787 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:55:30.278798 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000448706
I0407 14:55:30.278811 32304 sgd_solver.cpp:106] Iteration 63000, lr = 0.00874
I0407 14:56:42.484531 32304 solver.cpp:229] Iteration 63500, loss = 0.85685
I0407 14:56:42.484650 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 14:56:42.484669 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.21875
I0407 14:56:42.484683 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 14:56:42.484694 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 14:56:42.484707 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 14:56:42.484719 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.25
I0407 14:56:42.484731 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 14:56:42.484743 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:56:42.484755 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 14:56:42.484766 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:56:42.484777 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:56:42.484789 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:56:42.484800 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:56:42.484812 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:56:42.484822 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:56:42.484834 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:56:42.484845 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:56:42.484858 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:56:42.484869 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:56:42.484880 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:56:42.484891 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:56:42.484904 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:56:42.484921 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.79888 (* 0.0454545 = 0.127222 loss)
I0407 14:56:42.484937 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.98881 (* 0.0454545 = 0.135855 loss)
I0407 14:56:42.484951 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.23666 (* 0.0454545 = 0.147121 loss)
I0407 14:56:42.484966 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.0625 (* 0.0454545 = 0.139205 loss)
I0407 14:56:42.484979 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.70597 (* 0.0454545 = 0.122999 loss)
I0407 14:56:42.484993 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.55704 (* 0.0454545 = 0.116229 loss)
I0407 14:56:42.485007 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.1421 (* 0.0454545 = 0.0519138 loss)
I0407 14:56:42.485019 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.491686 (* 0.0454545 = 0.0223494 loss)
I0407 14:56:42.485034 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0119306 (* 0.0454545 = 0.000542301 loss)
I0407 14:56:42.485049 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0051114 (* 0.0454545 = 0.000232336 loss)
I0407 14:56:42.485062 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 6.98735e-05 (* 0.0454545 = 3.17607e-06 loss)
I0407 14:56:42.485076 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 6.13412e-05 (* 0.0454545 = 2.78824e-06 loss)
I0407 14:56:42.485090 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 6.41229e-05 (* 0.0454545 = 2.91468e-06 loss)
I0407 14:56:42.485105 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 6.82585e-05 (* 0.0454545 = 3.10266e-06 loss)
I0407 14:56:42.485118 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 6.53318e-05 (* 0.0454545 = 2.96963e-06 loss)
I0407 14:56:42.485132 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.26882e-05 (* 0.0454545 = 2.84946e-06 loss)
I0407 14:56:42.485146 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.77666e-05 (* 0.0454545 = 3.0803e-06 loss)
I0407 14:56:42.485177 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 6.82923e-05 (* 0.0454545 = 3.10419e-06 loss)
I0407 14:56:42.485191 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.49384e-05 (* 0.0454545 = 2.95175e-06 loss)
I0407 14:56:42.485205 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 6.33402e-05 (* 0.0454545 = 2.8791e-06 loss)
I0407 14:56:42.485219 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.28447e-05 (* 0.0454545 = 2.85658e-06 loss)
I0407 14:56:42.485234 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.07751e-05 (* 0.0454545 = 2.76251e-06 loss)
I0407 14:56:42.485245 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:56:42.485256 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000341331
I0407 14:56:42.485270 32304 sgd_solver.cpp:106] Iteration 63500, lr = 0.00873
I0407 14:57:54.588377 32304 solver.cpp:229] Iteration 64000, loss = 0.850585
I0407 14:57:54.588510 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.15625
I0407 14:57:54.588531 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:57:54.588543 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 14:57:54.588557 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 14:57:54.588568 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.34375
I0407 14:57:54.588580 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 14:57:54.588593 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 14:57:54.588603 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 14:57:54.588615 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 14:57:54.588626 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:57:54.588639 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:57:54.588649 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:57:54.588661 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:57:54.588672 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:57:54.588683 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:57:54.588695 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:57:54.588706 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:57:54.588717 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:57:54.588728 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:57:54.588739 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:57:54.588750 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:57:54.588762 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:57:54.588778 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.60098 (* 0.0454545 = 0.118226 loss)
I0407 14:57:54.588793 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.05795 (* 0.0454545 = 0.138998 loss)
I0407 14:57:54.588806 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.11844 (* 0.0454545 = 0.141747 loss)
I0407 14:57:54.588819 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.95665 (* 0.0454545 = 0.134393 loss)
I0407 14:57:54.588834 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.33634 (* 0.0454545 = 0.106197 loss)
I0407 14:57:54.588846 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.1112 (* 0.0454545 = 0.0959638 loss)
I0407 14:57:54.588860 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.10004 (* 0.0454545 = 0.0500017 loss)
I0407 14:57:54.588874 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.386515 (* 0.0454545 = 0.0175688 loss)
I0407 14:57:54.588888 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.175441 (* 0.0454545 = 0.00797458 loss)
I0407 14:57:54.588902 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0135668 (* 0.0454545 = 0.000616673 loss)
I0407 14:57:54.588917 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 9.00863e-05 (* 0.0454545 = 4.09483e-06 loss)
I0407 14:57:54.588934 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.909e-05 (* 0.0454545 = 3.595e-06 loss)
I0407 14:57:54.588949 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 8.09939e-05 (* 0.0454545 = 3.68154e-06 loss)
I0407 14:57:54.588963 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 7.61562e-05 (* 0.0454545 = 3.46165e-06 loss)
I0407 14:57:54.588976 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.75981e-05 (* 0.0454545 = 3.52719e-06 loss)
I0407 14:57:54.588990 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 7.95487e-05 (* 0.0454545 = 3.61585e-06 loss)
I0407 14:57:54.589004 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.37628e-05 (* 0.0454545 = 2.89831e-06 loss)
I0407 14:57:54.589031 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 8.73207e-05 (* 0.0454545 = 3.96912e-06 loss)
I0407 14:57:54.589046 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.80453e-05 (* 0.0454545 = 3.54751e-06 loss)
I0407 14:57:54.589061 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.46749e-05 (* 0.0454545 = 3.39431e-06 loss)
I0407 14:57:54.589074 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.99466e-05 (* 0.0454545 = 3.17939e-06 loss)
I0407 14:57:54.589087 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.9941e-05 (* 0.0454545 = 3.17914e-06 loss)
I0407 14:57:54.589099 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:57:54.589112 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000423907
I0407 14:57:54.589124 32304 sgd_solver.cpp:106] Iteration 64000, lr = 0.00872
I0407 14:59:06.683472 32304 solver.cpp:229] Iteration 64500, loss = 0.850672
I0407 14:59:06.683619 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.125
I0407 14:59:06.683639 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 14:59:06.683652 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 14:59:06.683665 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 14:59:06.683676 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.125
I0407 14:59:06.683687 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 14:59:06.683699 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.875
I0407 14:59:06.683712 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 14:59:06.683723 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 14:59:06.683735 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 14:59:06.683746 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 14:59:06.683758 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 14:59:06.683769 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 14:59:06.683780 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 14:59:06.683791 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 14:59:06.683802 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 14:59:06.683815 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 14:59:06.683825 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 14:59:06.683837 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 14:59:06.683848 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 14:59:06.683859 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 14:59:06.683871 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 14:59:06.683887 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 3.07035 (* 0.0454545 = 0.139562 loss)
I0407 14:59:06.683902 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.18164 (* 0.0454545 = 0.14462 loss)
I0407 14:59:06.683915 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.25657 (* 0.0454545 = 0.148026 loss)
I0407 14:59:06.683933 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.28923 (* 0.0454545 = 0.14951 loss)
I0407 14:59:06.683946 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.11273 (* 0.0454545 = 0.141488 loss)
I0407 14:59:06.683960 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.31593 (* 0.0454545 = 0.105269 loss)
I0407 14:59:06.683974 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.550537 (* 0.0454545 = 0.0250244 loss)
I0407 14:59:06.683987 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.259313 (* 0.0454545 = 0.011787 loss)
I0407 14:59:06.684001 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0811856 (* 0.0454545 = 0.00369026 loss)
I0407 14:59:06.684015 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0236179 (* 0.0454545 = 0.00107354 loss)
I0407 14:59:06.684031 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000152127 (* 0.0454545 = 6.91484e-06 loss)
I0407 14:59:06.684044 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000144789 (* 0.0454545 = 6.58131e-06 loss)
I0407 14:59:06.684057 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000143248 (* 0.0454545 = 6.51127e-06 loss)
I0407 14:59:06.684072 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000138491 (* 0.0454545 = 6.29503e-06 loss)
I0407 14:59:06.684087 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000144002 (* 0.0454545 = 6.54557e-06 loss)
I0407 14:59:06.684099 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000133377 (* 0.0454545 = 6.06259e-06 loss)
I0407 14:59:06.684113 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000125773 (* 0.0454545 = 5.71696e-06 loss)
I0407 14:59:06.684144 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000149667 (* 0.0454545 = 6.80304e-06 loss)
I0407 14:59:06.684159 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000132986 (* 0.0454545 = 6.04483e-06 loss)
I0407 14:59:06.684173 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.0001304 (* 0.0454545 = 5.92729e-06 loss)
I0407 14:59:06.684187 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000130617 (* 0.0454545 = 5.93714e-06 loss)
I0407 14:59:06.684201 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000124749 (* 0.0454545 = 5.67042e-06 loss)
I0407 14:59:06.684213 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 14:59:06.684226 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000255986
I0407 14:59:06.684239 32304 sgd_solver.cpp:106] Iteration 64500, lr = 0.00871
I0407 15:00:18.813752 32304 solver.cpp:338] Iteration 65000, Testing net (#0)
I0407 15:00:26.789994 32304 solver.cpp:393] Test loss: 0.751224
I0407 15:00:26.790040 32304 solver.cpp:406]     Test net output #0: loss/accuracy01 = 0.22
I0407 15:00:26.790057 32304 solver.cpp:406]     Test net output #1: loss/accuracy02 = 0.109
I0407 15:00:26.790071 32304 solver.cpp:406]     Test net output #2: loss/accuracy03 = 0.118
I0407 15:00:26.790082 32304 solver.cpp:406]     Test net output #3: loss/accuracy04 = 0.151
I0407 15:00:26.790093 32304 solver.cpp:406]     Test net output #4: loss/accuracy05 = 0.254
I0407 15:00:26.790105 32304 solver.cpp:406]     Test net output #5: loss/accuracy06 = 0.526
I0407 15:00:26.790117 32304 solver.cpp:406]     Test net output #6: loss/accuracy07 = 0.897
I0407 15:00:26.790127 32304 solver.cpp:406]     Test net output #7: loss/accuracy08 = 0.97
I0407 15:00:26.790138 32304 solver.cpp:406]     Test net output #8: loss/accuracy09 = 0.995
I0407 15:00:26.790150 32304 solver.cpp:406]     Test net output #9: loss/accuracy10 = 0.998
I0407 15:00:26.790161 32304 solver.cpp:406]     Test net output #10: loss/accuracy11 = 1
I0407 15:00:26.790172 32304 solver.cpp:406]     Test net output #11: loss/accuracy12 = 1
I0407 15:00:26.790184 32304 solver.cpp:406]     Test net output #12: loss/accuracy13 = 1
I0407 15:00:26.790195 32304 solver.cpp:406]     Test net output #13: loss/accuracy14 = 1
I0407 15:00:26.790206 32304 solver.cpp:406]     Test net output #14: loss/accuracy15 = 1
I0407 15:00:26.790217 32304 solver.cpp:406]     Test net output #15: loss/accuracy16 = 1
I0407 15:00:26.790228 32304 solver.cpp:406]     Test net output #16: loss/accuracy17 = 1
I0407 15:00:26.790240 32304 solver.cpp:406]     Test net output #17: loss/accuracy18 = 1
I0407 15:00:26.790251 32304 solver.cpp:406]     Test net output #18: loss/accuracy19 = 1
I0407 15:00:26.790262 32304 solver.cpp:406]     Test net output #19: loss/accuracy20 = 1
I0407 15:00:26.790273 32304 solver.cpp:406]     Test net output #20: loss/accuracy21 = 1
I0407 15:00:26.790284 32304 solver.cpp:406]     Test net output #21: loss/accuracy22 = 1
I0407 15:00:26.790299 32304 solver.cpp:406]     Test net output #22: loss/loss01 = 2.77803 (* 0.0454545 = 0.126274 loss)
I0407 15:00:26.790313 32304 solver.cpp:406]     Test net output #23: loss/loss02 = 2.93853 (* 0.0454545 = 0.13357 loss)
I0407 15:00:26.790328 32304 solver.cpp:406]     Test net output #24: loss/loss03 = 2.92924 (* 0.0454545 = 0.133147 loss)
I0407 15:00:26.790340 32304 solver.cpp:406]     Test net output #25: loss/loss04 = 2.86099 (* 0.0454545 = 0.130045 loss)
I0407 15:00:26.790354 32304 solver.cpp:406]     Test net output #26: loss/loss05 = 2.62577 (* 0.0454545 = 0.119353 loss)
I0407 15:00:26.790367 32304 solver.cpp:406]     Test net output #27: loss/loss06 = 1.68763 (* 0.0454545 = 0.0767104 loss)
I0407 15:00:26.790380 32304 solver.cpp:406]     Test net output #28: loss/loss07 = 0.429252 (* 0.0454545 = 0.0195115 loss)
I0407 15:00:26.790395 32304 solver.cpp:406]     Test net output #29: loss/loss08 = 0.201378 (* 0.0454545 = 0.00915356 loss)
I0407 15:00:26.790408 32304 solver.cpp:406]     Test net output #30: loss/loss09 = 0.0523305 (* 0.0454545 = 0.00237866 loss)
I0407 15:00:26.790422 32304 solver.cpp:406]     Test net output #31: loss/loss10 = 0.0228979 (* 0.0454545 = 0.00104081 loss)
I0407 15:00:26.790436 32304 solver.cpp:406]     Test net output #32: loss/loss11 = 7.4944e-05 (* 0.0454545 = 3.40654e-06 loss)
I0407 15:00:26.790449 32304 solver.cpp:406]     Test net output #33: loss/loss12 = 7.5806e-05 (* 0.0454545 = 3.44573e-06 loss)
I0407 15:00:26.790463 32304 solver.cpp:406]     Test net output #34: loss/loss13 = 7.58695e-05 (* 0.0454545 = 3.44862e-06 loss)
I0407 15:00:26.790477 32304 solver.cpp:406]     Test net output #35: loss/loss14 = 7.48943e-05 (* 0.0454545 = 3.40429e-06 loss)
I0407 15:00:26.790490 32304 solver.cpp:406]     Test net output #36: loss/loss15 = 7.09186e-05 (* 0.0454545 = 3.22357e-06 loss)
I0407 15:00:26.790504 32304 solver.cpp:406]     Test net output #37: loss/loss16 = 7.66309e-05 (* 0.0454545 = 3.48322e-06 loss)
I0407 15:00:26.790518 32304 solver.cpp:406]     Test net output #38: loss/loss17 = 7.47564e-05 (* 0.0454545 = 3.39802e-06 loss)
I0407 15:00:26.790567 32304 solver.cpp:406]     Test net output #39: loss/loss18 = 6.98005e-05 (* 0.0454545 = 3.17275e-06 loss)
I0407 15:00:26.790582 32304 solver.cpp:406]     Test net output #40: loss/loss19 = 7.29501e-05 (* 0.0454545 = 3.31591e-06 loss)
I0407 15:00:26.790596 32304 solver.cpp:406]     Test net output #41: loss/loss20 = 7.53733e-05 (* 0.0454545 = 3.42606e-06 loss)
I0407 15:00:26.790611 32304 solver.cpp:406]     Test net output #42: loss/loss21 = 6.83761e-05 (* 0.0454545 = 3.108e-06 loss)
I0407 15:00:26.790624 32304 solver.cpp:406]     Test net output #43: loss/loss22 = 7.333e-05 (* 0.0454545 = 3.33318e-06 loss)
I0407 15:00:26.790637 32304 solver.cpp:406]     Test net output #44: total_accuracy = 0.001
I0407 15:00:26.790647 32304 solver.cpp:406]     Test net output #45: total_confidence = 0.000563006
I0407 15:00:26.824812 32304 solver.cpp:229] Iteration 65000, loss = 0.846674
I0407 15:00:26.824851 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.3125
I0407 15:00:26.824867 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.25
I0407 15:00:26.824880 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.1875
I0407 15:00:26.824892 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.25
I0407 15:00:26.824903 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 15:00:26.824916 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.3125
I0407 15:00:26.824928 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.625
I0407 15:00:26.824939 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 15:00:26.824950 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 15:00:26.824962 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 15:00:26.824973 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 15:00:26.824985 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 15:00:26.824996 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 15:00:26.825008 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 15:00:26.825019 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 15:00:26.825031 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 15:00:26.825042 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 15:00:26.825052 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 15:00:26.825063 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 15:00:26.825078 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 15:00:26.825089 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 15:00:26.825100 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 15:00:26.825115 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.80358 (* 0.0454545 = 0.127436 loss)
I0407 15:00:26.825129 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.61543 (* 0.0454545 = 0.118883 loss)
I0407 15:00:26.825144 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.09636 (* 0.0454545 = 0.140744 loss)
I0407 15:00:26.825156 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.8554 (* 0.0454545 = 0.129791 loss)
I0407 15:00:26.825170 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.81525 (* 0.0454545 = 0.127966 loss)
I0407 15:00:26.825183 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.3381 (* 0.0454545 = 0.106277 loss)
I0407 15:00:26.825196 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.64966 (* 0.0454545 = 0.0749847 loss)
I0407 15:00:26.825211 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.607301 (* 0.0454545 = 0.0276046 loss)
I0407 15:00:26.825223 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.166366 (* 0.0454545 = 0.00756208 loss)
I0407 15:00:26.825254 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.133073 (* 0.0454545 = 0.00604876 loss)
I0407 15:00:26.825270 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 3.65549e-05 (* 0.0454545 = 1.66158e-06 loss)
I0407 15:00:26.825284 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 3.71361e-05 (* 0.0454545 = 1.688e-06 loss)
I0407 15:00:26.825299 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 3.32519e-05 (* 0.0454545 = 1.51145e-06 loss)
I0407 15:00:26.825314 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 3.65663e-05 (* 0.0454545 = 1.66211e-06 loss)
I0407 15:00:26.825327 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 3.67152e-05 (* 0.0454545 = 1.66887e-06 loss)
I0407 15:00:26.825340 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 3.15158e-05 (* 0.0454545 = 1.43253e-06 loss)
I0407 15:00:26.825355 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 3.2235e-05 (* 0.0454545 = 1.46523e-06 loss)
I0407 15:00:26.825368 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 3.60892e-05 (* 0.0454545 = 1.64042e-06 loss)
I0407 15:00:26.825382 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 3.56719e-05 (* 0.0454545 = 1.62145e-06 loss)
I0407 15:00:26.825397 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 3.48784e-05 (* 0.0454545 = 1.58538e-06 loss)
I0407 15:00:26.825410 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 3.24212e-05 (* 0.0454545 = 1.47369e-06 loss)
I0407 15:00:26.825423 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 3.27472e-05 (* 0.0454545 = 1.48851e-06 loss)
I0407 15:00:26.825435 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 15:00:26.825448 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00139197
I0407 15:00:26.825461 32304 sgd_solver.cpp:106] Iteration 65000, lr = 0.0087
I0407 15:01:39.858453 32304 solver.cpp:229] Iteration 65500, loss = 0.852158
I0407 15:01:39.858595 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.25
I0407 15:01:39.858615 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 15:01:39.858629 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 15:01:39.858641 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.28125
I0407 15:01:39.858654 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 15:01:39.858665 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 15:01:39.858677 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.5625
I0407 15:01:39.858690 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 15:01:39.858701 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 15:01:39.858713 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 15:01:39.858726 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 15:01:39.858737 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 15:01:39.858748 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 15:01:39.858759 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 15:01:39.858770 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 15:01:39.858782 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 15:01:39.858793 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 15:01:39.858803 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 15:01:39.858815 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 15:01:39.858826 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 15:01:39.858837 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 15:01:39.858849 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 15:01:39.858865 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.60962 (* 0.0454545 = 0.118619 loss)
I0407 15:01:39.858878 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.89078 (* 0.0454545 = 0.131399 loss)
I0407 15:01:39.858892 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.05496 (* 0.0454545 = 0.138862 loss)
I0407 15:01:39.858906 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.90741 (* 0.0454545 = 0.132155 loss)
I0407 15:01:39.858922 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.60018 (* 0.0454545 = 0.11819 loss)
I0407 15:01:39.858937 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.66762 (* 0.0454545 = 0.121255 loss)
I0407 15:01:39.858950 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.83936 (* 0.0454545 = 0.0836071 loss)
I0407 15:01:39.858964 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.574745 (* 0.0454545 = 0.0261248 loss)
I0407 15:01:39.858978 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.424202 (* 0.0454545 = 0.0192819 loss)
I0407 15:01:39.858991 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.123639 (* 0.0454545 = 0.00561995 loss)
I0407 15:01:39.859006 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.01998e-05 (* 0.0454545 = 3.64544e-06 loss)
I0407 15:01:39.859020 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 8.51606e-05 (* 0.0454545 = 3.87094e-06 loss)
I0407 15:01:39.859035 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.71112e-05 (* 0.0454545 = 3.50505e-06 loss)
I0407 15:01:39.859048 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.16899e-05 (* 0.0454545 = 3.71318e-06 loss)
I0407 15:01:39.859062 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.07024e-05 (* 0.0454545 = 3.21375e-06 loss)
I0407 15:01:39.859076 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.81828e-05 (* 0.0454545 = 3.09922e-06 loss)
I0407 15:01:39.859089 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.78138e-05 (* 0.0454545 = 3.08245e-06 loss)
I0407 15:01:39.859117 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.48806e-05 (* 0.0454545 = 3.40366e-06 loss)
I0407 15:01:39.859132 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.34541e-05 (* 0.0454545 = 3.33882e-06 loss)
I0407 15:01:39.859145 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.36496e-05 (* 0.0454545 = 3.34771e-06 loss)
I0407 15:01:39.859159 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.96809e-05 (* 0.0454545 = 3.16731e-06 loss)
I0407 15:01:39.859172 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.17998e-05 (* 0.0454545 = 3.26363e-06 loss)
I0407 15:01:39.859184 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 15:01:39.859196 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000679538
I0407 15:01:39.859210 32304 sgd_solver.cpp:106] Iteration 65500, lr = 0.00869
I0407 15:02:53.939813 32304 solver.cpp:229] Iteration 66000, loss = 0.847152
I0407 15:02:53.940101 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.25
I0407 15:02:53.940121 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 15:02:53.940135 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.21875
I0407 15:02:53.940147 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 15:02:53.940160 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.15625
I0407 15:02:53.940181 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.375
I0407 15:02:53.940203 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 15:02:53.940218 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 15:02:53.940230 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.90625
I0407 15:02:53.940243 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 15:02:53.940253 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 15:02:53.940265 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 15:02:53.940276 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 15:02:53.940289 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 15:02:53.940299 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 15:02:53.940310 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 15:02:53.940321 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 15:02:53.940333 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 15:02:53.940345 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 15:02:53.940356 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 15:02:53.940366 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 15:02:53.940378 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 15:02:53.940393 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.93097 (* 0.0454545 = 0.133226 loss)
I0407 15:02:53.940407 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.00872 (* 0.0454545 = 0.13676 loss)
I0407 15:02:53.940421 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.11235 (* 0.0454545 = 0.14147 loss)
I0407 15:02:53.940435 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.3768 (* 0.0454545 = 0.153491 loss)
I0407 15:02:53.940449 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 3.1463 (* 0.0454545 = 0.143014 loss)
I0407 15:02:53.940462 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.41549 (* 0.0454545 = 0.109795 loss)
I0407 15:02:53.940476 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.21036 (* 0.0454545 = 0.0550162 loss)
I0407 15:02:53.940490 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.409821 (* 0.0454545 = 0.0186282 loss)
I0407 15:02:53.940503 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.421244 (* 0.0454545 = 0.0191474 loss)
I0407 15:02:53.940517 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.217774 (* 0.0454545 = 0.0098988 loss)
I0407 15:02:53.940531 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 9.44928e-05 (* 0.0454545 = 4.29513e-06 loss)
I0407 15:02:53.940546 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 9.03593e-05 (* 0.0454545 = 4.10724e-06 loss)
I0407 15:02:53.940560 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 8.49584e-05 (* 0.0454545 = 3.86175e-06 loss)
I0407 15:02:53.940573 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.63894e-05 (* 0.0454545 = 3.92679e-06 loss)
I0407 15:02:53.940587 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 8.34481e-05 (* 0.0454545 = 3.7931e-06 loss)
I0407 15:02:53.940601 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 7.26942e-05 (* 0.0454545 = 3.30428e-06 loss)
I0407 15:02:53.940615 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 7.79911e-05 (* 0.0454545 = 3.54505e-06 loss)
I0407 15:02:53.940647 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.61931e-05 (* 0.0454545 = 3.46332e-06 loss)
I0407 15:02:53.940662 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 7.82159e-05 (* 0.0454545 = 3.55527e-06 loss)
I0407 15:02:53.940676 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 7.84636e-05 (* 0.0454545 = 3.56653e-06 loss)
I0407 15:02:53.940690 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 8.64167e-05 (* 0.0454545 = 3.92803e-06 loss)
I0407 15:02:53.940703 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.97528e-05 (* 0.0454545 = 3.17058e-06 loss)
I0407 15:02:53.940716 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 15:02:53.940726 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00086436
I0407 15:02:53.940739 32304 sgd_solver.cpp:106] Iteration 66000, lr = 0.00868
I0407 15:04:06.356780 32304 solver.cpp:229] Iteration 66500, loss = 0.850381
I0407 15:04:06.356880 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.28125
I0407 15:04:06.356899 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0
I0407 15:04:06.356912 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.09375
I0407 15:04:06.356925 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.0625
I0407 15:04:06.356936 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.28125
I0407 15:04:06.356948 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 15:04:06.356961 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.8125
I0407 15:04:06.356972 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 15:04:06.356983 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 15:04:06.356995 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 15:04:06.357007 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 15:04:06.357018 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 15:04:06.357029 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 15:04:06.357040 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 15:04:06.357051 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 15:04:06.357064 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 15:04:06.357077 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 15:04:06.357089 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 15:04:06.357100 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 15:04:06.357111 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 15:04:06.357122 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 15:04:06.357134 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 15:04:06.357151 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.20731 (* 0.0454545 = 0.100332 loss)
I0407 15:04:06.357164 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.00897 (* 0.0454545 = 0.136771 loss)
I0407 15:04:06.357178 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.08744 (* 0.0454545 = 0.140338 loss)
I0407 15:04:06.357192 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.03842 (* 0.0454545 = 0.13811 loss)
I0407 15:04:06.357205 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.79099 (* 0.0454545 = 0.126863 loss)
I0407 15:04:06.357219 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.06719 (* 0.0454545 = 0.093963 loss)
I0407 15:04:06.357233 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.805061 (* 0.0454545 = 0.0365937 loss)
I0407 15:04:06.357246 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.266289 (* 0.0454545 = 0.012104 loss)
I0407 15:04:06.357260 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.194756 (* 0.0454545 = 0.00885256 loss)
I0407 15:04:06.357275 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00403786 (* 0.0454545 = 0.000183539 loss)
I0407 15:04:06.357288 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 2.86766e-05 (* 0.0454545 = 1.30348e-06 loss)
I0407 15:04:06.357302 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 2.89579e-05 (* 0.0454545 = 1.31627e-06 loss)
I0407 15:04:06.357316 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 2.59234e-05 (* 0.0454545 = 1.17834e-06 loss)
I0407 15:04:06.357331 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 2.806e-05 (* 0.0454545 = 1.27545e-06 loss)
I0407 15:04:06.357344 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 2.73615e-05 (* 0.0454545 = 1.24371e-06 loss)
I0407 15:04:06.357358 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 2.65046e-05 (* 0.0454545 = 1.20475e-06 loss)
I0407 15:04:06.357372 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 2.80062e-05 (* 0.0454545 = 1.27301e-06 loss)
I0407 15:04:06.357403 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 2.76725e-05 (* 0.0454545 = 1.25784e-06 loss)
I0407 15:04:06.357419 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 2.80433e-05 (* 0.0454545 = 1.27469e-06 loss)
I0407 15:04:06.357432 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 2.79576e-05 (* 0.0454545 = 1.2708e-06 loss)
I0407 15:04:06.357446 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 2.56458e-05 (* 0.0454545 = 1.16572e-06 loss)
I0407 15:04:06.357460 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 2.49418e-05 (* 0.0454545 = 1.13372e-06 loss)
I0407 15:04:06.357471 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 15:04:06.357483 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00154345
I0407 15:04:06.357507 32304 sgd_solver.cpp:106] Iteration 66500, lr = 0.00867
I0407 15:05:18.559504 32304 solver.cpp:229] Iteration 67000, loss = 0.847989
I0407 15:05:18.559631 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.3125
I0407 15:05:18.559651 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 15:05:18.559664 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.1875
I0407 15:05:18.559676 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.15625
I0407 15:05:18.559689 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.46875
I0407 15:05:18.559700 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.4375
I0407 15:05:18.559711 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 15:05:18.559723 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.90625
I0407 15:05:18.559736 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 15:05:18.559747 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 15:05:18.559758 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 15:05:18.559769 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 15:05:18.559780 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 15:05:18.559792 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 15:05:18.559803 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 15:05:18.559814 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 15:05:18.559825 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 15:05:18.559837 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 15:05:18.559849 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 15:05:18.559859 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 15:05:18.559870 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 15:05:18.559882 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 15:05:18.559897 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.28767 (* 0.0454545 = 0.103985 loss)
I0407 15:05:18.559911 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.12414 (* 0.0454545 = 0.142007 loss)
I0407 15:05:18.559926 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.09311 (* 0.0454545 = 0.140596 loss)
I0407 15:05:18.559938 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.81299 (* 0.0454545 = 0.127863 loss)
I0407 15:05:18.559952 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 1.9361 (* 0.0454545 = 0.0880044 loss)
I0407 15:05:18.559967 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.1008 (* 0.0454545 = 0.0954908 loss)
I0407 15:05:18.559980 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.971732 (* 0.0454545 = 0.0441696 loss)
I0407 15:05:18.559993 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.357065 (* 0.0454545 = 0.0162302 loss)
I0407 15:05:18.560008 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.150945 (* 0.0454545 = 0.00686116 loss)
I0407 15:05:18.560021 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.00322226 (* 0.0454545 = 0.000146466 loss)
I0407 15:05:18.560035 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.26675e-05 (* 0.0454545 = 3.75761e-06 loss)
I0407 15:05:18.560050 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 9.07038e-05 (* 0.0454545 = 4.1229e-06 loss)
I0407 15:05:18.560065 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.66612e-05 (* 0.0454545 = 3.4846e-06 loss)
I0407 15:05:18.560081 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 8.78773e-05 (* 0.0454545 = 3.99442e-06 loss)
I0407 15:05:18.560096 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 8.67346e-05 (* 0.0454545 = 3.94248e-06 loss)
I0407 15:05:18.560109 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 7.71247e-05 (* 0.0454545 = 3.50567e-06 loss)
I0407 15:05:18.560123 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 7.13525e-05 (* 0.0454545 = 3.2433e-06 loss)
I0407 15:05:18.560153 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 8.994e-05 (* 0.0454545 = 4.08818e-06 loss)
I0407 15:05:18.560173 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 8.58671e-05 (* 0.0454545 = 3.90305e-06 loss)
I0407 15:05:18.560187 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 9.27823e-05 (* 0.0454545 = 4.21738e-06 loss)
I0407 15:05:18.560201 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 7.45459e-05 (* 0.0454545 = 3.38845e-06 loss)
I0407 15:05:18.560214 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 7.85979e-05 (* 0.0454545 = 3.57263e-06 loss)
I0407 15:05:18.560226 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 15:05:18.560238 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00227327
I0407 15:05:18.560251 32304 sgd_solver.cpp:106] Iteration 67000, lr = 0.00866
I0407 15:06:30.765231 32304 solver.cpp:229] Iteration 67500, loss = 0.843809
I0407 15:06:30.765353 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.375
I0407 15:06:30.765372 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 15:06:30.765385 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 15:06:30.765400 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.1875
I0407 15:06:30.765413 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.25
I0407 15:06:30.765425 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.34375
I0407 15:06:30.765437 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 15:06:30.765450 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.84375
I0407 15:06:30.765461 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 15:06:30.765473 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 15:06:30.765485 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 15:06:30.765496 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 15:06:30.765507 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 15:06:30.765519 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 15:06:30.765530 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 15:06:30.765542 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 15:06:30.765552 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 15:06:30.765564 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 15:06:30.765575 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 15:06:30.765586 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 15:06:30.765597 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 15:06:30.765609 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 15:06:30.765625 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.4718 (* 0.0454545 = 0.112354 loss)
I0407 15:06:30.765640 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.02427 (* 0.0454545 = 0.137467 loss)
I0407 15:06:30.765652 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.1045 (* 0.0454545 = 0.141114 loss)
I0407 15:06:30.765666 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.9348 (* 0.0454545 = 0.1334 loss)
I0407 15:06:30.765681 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.57848 (* 0.0454545 = 0.117204 loss)
I0407 15:06:30.765693 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.3091 (* 0.0454545 = 0.104959 loss)
I0407 15:06:30.765707 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.09498 (* 0.0454545 = 0.0497716 loss)
I0407 15:06:30.765720 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.78772 (* 0.0454545 = 0.0358054 loss)
I0407 15:06:30.765734 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.141134 (* 0.0454545 = 0.00641519 loss)
I0407 15:06:30.765748 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.016371 (* 0.0454545 = 0.000744138 loss)
I0407 15:06:30.765763 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000112144 (* 0.0454545 = 5.09746e-06 loss)
I0407 15:06:30.765776 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000110107 (* 0.0454545 = 5.00486e-06 loss)
I0407 15:06:30.765790 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.00010816 (* 0.0454545 = 4.91637e-06 loss)
I0407 15:06:30.765805 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00011346 (* 0.0454545 = 5.15727e-06 loss)
I0407 15:06:30.765820 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000106706 (* 0.0454545 = 4.85028e-06 loss)
I0407 15:06:30.765833 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000101808 (* 0.0454545 = 4.62765e-06 loss)
I0407 15:06:30.765846 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 9.84663e-05 (* 0.0454545 = 4.47574e-06 loss)
I0407 15:06:30.765874 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000113811 (* 0.0454545 = 5.17322e-06 loss)
I0407 15:06:30.765889 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.0001117 (* 0.0454545 = 5.07728e-06 loss)
I0407 15:06:30.765903 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.00010829 (* 0.0454545 = 4.92229e-06 loss)
I0407 15:06:30.765918 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000103323 (* 0.0454545 = 4.69648e-06 loss)
I0407 15:06:30.765930 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 9.93589e-05 (* 0.0454545 = 4.51631e-06 loss)
I0407 15:06:30.765943 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 15:06:30.765954 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00113089
I0407 15:06:30.765966 32304 sgd_solver.cpp:106] Iteration 67500, lr = 0.00865
I0407 15:07:42.995110 32304 solver.cpp:229] Iteration 68000, loss = 0.8445
I0407 15:07:42.995218 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.21875
I0407 15:07:42.995235 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.125
I0407 15:07:42.995249 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.03125
I0407 15:07:42.995260 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.125
I0407 15:07:42.995272 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.3125
I0407 15:07:42.995285 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 15:07:42.995296 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.71875
I0407 15:07:42.995308 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 15:07:42.995319 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.9375
I0407 15:07:42.995332 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 15:07:42.995357 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 15:07:42.995371 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 15:07:42.995383 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 15:07:42.995395 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 15:07:42.995406 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 15:07:42.995417 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 15:07:42.995429 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 15:07:42.995440 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 15:07:42.995451 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 15:07:42.995463 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 15:07:42.995474 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 15:07:42.995486 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 15:07:42.995502 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.54708 (* 0.0454545 = 0.115777 loss)
I0407 15:07:42.995517 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.03012 (* 0.0454545 = 0.137733 loss)
I0407 15:07:42.995530 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 3.17673 (* 0.0454545 = 0.144397 loss)
I0407 15:07:42.995543 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.92661 (* 0.0454545 = 0.133028 loss)
I0407 15:07:42.995556 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.65506 (* 0.0454545 = 0.120684 loss)
I0407 15:07:42.995570 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.3528 (* 0.0454545 = 0.106946 loss)
I0407 15:07:42.995584 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.08068 (* 0.0454545 = 0.0491216 loss)
I0407 15:07:42.995597 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.587965 (* 0.0454545 = 0.0267257 loss)
I0407 15:07:42.995610 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.274432 (* 0.0454545 = 0.0124742 loss)
I0407 15:07:42.995625 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.240431 (* 0.0454545 = 0.0109287 loss)
I0407 15:07:42.995638 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 5.58641e-05 (* 0.0454545 = 2.53928e-06 loss)
I0407 15:07:42.995652 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 5.08679e-05 (* 0.0454545 = 2.31218e-06 loss)
I0407 15:07:42.995666 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 5.22279e-05 (* 0.0454545 = 2.374e-06 loss)
I0407 15:07:42.995679 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 5.25298e-05 (* 0.0454545 = 2.38772e-06 loss)
I0407 15:07:42.995693 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 4.88189e-05 (* 0.0454545 = 2.21904e-06 loss)
I0407 15:07:42.995707 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 4.55495e-05 (* 0.0454545 = 2.07043e-06 loss)
I0407 15:07:42.995721 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 4.95547e-05 (* 0.0454545 = 2.25249e-06 loss)
I0407 15:07:42.995753 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 4.55644e-05 (* 0.0454545 = 2.07111e-06 loss)
I0407 15:07:42.995767 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 4.97593e-05 (* 0.0454545 = 2.26179e-06 loss)
I0407 15:07:42.995780 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 5.1058e-05 (* 0.0454545 = 2.32082e-06 loss)
I0407 15:07:42.995795 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 4.74422e-05 (* 0.0454545 = 2.15646e-06 loss)
I0407 15:07:42.995807 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 4.48266e-05 (* 0.0454545 = 2.03757e-06 loss)
I0407 15:07:42.995822 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 15:07:42.995834 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000759899
I0407 15:07:42.995848 32304 sgd_solver.cpp:106] Iteration 68000, lr = 0.00864
I0407 15:08:54.982893 32304 solver.cpp:229] Iteration 68500, loss = 0.84276
I0407 15:08:54.983001 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.1875
I0407 15:08:54.983021 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.09375
I0407 15:08:54.983037 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.0625
I0407 15:08:54.983049 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 15:08:54.983062 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.21875
I0407 15:08:54.983073 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.40625
I0407 15:08:54.983085 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.78125
I0407 15:08:54.983098 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.875
I0407 15:08:54.983108 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 1
I0407 15:08:54.983120 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 15:08:54.983132 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 15:08:54.983144 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 15:08:54.983156 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 15:08:54.983167 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 15:08:54.983178 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 15:08:54.983191 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 15:08:54.983201 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 15:08:54.983212 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 15:08:54.983223 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 15:08:54.983235 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 15:08:54.983247 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 15:08:54.983258 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 15:08:54.983273 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.75359 (* 0.0454545 = 0.125163 loss)
I0407 15:08:54.983288 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.01462 (* 0.0454545 = 0.137028 loss)
I0407 15:08:54.983301 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 2.89237 (* 0.0454545 = 0.131472 loss)
I0407 15:08:54.983315 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.97514 (* 0.0454545 = 0.135234 loss)
I0407 15:08:54.983351 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.63725 (* 0.0454545 = 0.119875 loss)
I0407 15:08:54.983366 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 2.29177 (* 0.0454545 = 0.104172 loss)
I0407 15:08:54.983379 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.926131 (* 0.0454545 = 0.0420969 loss)
I0407 15:08:54.983392 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.502783 (* 0.0454545 = 0.0228538 loss)
I0407 15:08:54.983407 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.0419899 (* 0.0454545 = 0.00190863 loss)
I0407 15:08:54.983422 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.018156 (* 0.0454545 = 0.000825274 loss)
I0407 15:08:54.983435 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000168967 (* 0.0454545 = 7.6803e-06 loss)
I0407 15:08:54.983449 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000181705 (* 0.0454545 = 8.25933e-06 loss)
I0407 15:08:54.983464 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000169843 (* 0.0454545 = 7.72013e-06 loss)
I0407 15:08:54.983477 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.000169745 (* 0.0454545 = 7.71569e-06 loss)
I0407 15:08:54.983491 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000160041 (* 0.0454545 = 7.27459e-06 loss)
I0407 15:08:54.983505 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000162851 (* 0.0454545 = 7.4023e-06 loss)
I0407 15:08:54.983520 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 0.000153592 (* 0.0454545 = 6.98147e-06 loss)
I0407 15:08:54.983551 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 0.000182217 (* 0.0454545 = 8.2826e-06 loss)
I0407 15:08:54.983567 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 0.000173222 (* 0.0454545 = 7.87374e-06 loss)
I0407 15:08:54.983580 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 0.000180153 (* 0.0454545 = 8.18878e-06 loss)
I0407 15:08:54.983594 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 0.000167564 (* 0.0454545 = 7.61653e-06 loss)
I0407 15:08:54.983608 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 0.000161796 (* 0.0454545 = 7.35436e-06 loss)
I0407 15:08:54.983620 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 15:08:54.983631 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.000546207
I0407 15:08:54.983645 32304 sgd_solver.cpp:106] Iteration 68500, lr = 0.00863
I0407 15:10:07.033193 32304 solver.cpp:229] Iteration 69000, loss = 0.843415
I0407 15:10:07.033313 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.21875
I0407 15:10:07.033334 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.0625
I0407 15:10:07.033347 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 15:10:07.033360 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.3125
I0407 15:10:07.033371 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.46875
I0407 15:10:07.033383 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.46875
I0407 15:10:07.033396 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.875
I0407 15:10:07.033406 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 15:10:07.033418 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 15:10:07.033430 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 0.96875
I0407 15:10:07.033442 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 15:10:07.033453 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 15:10:07.033465 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 15:10:07.033476 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 15:10:07.033488 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 15:10:07.033499 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 15:10:07.033509 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 15:10:07.033521 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 15:10:07.033532 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 15:10:07.033543 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 15:10:07.033555 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 15:10:07.033566 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 15:10:07.033581 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.32451 (* 0.0454545 = 0.105659 loss)
I0407 15:10:07.033596 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 3.01975 (* 0.0454545 = 0.137261 loss)
I0407 15:10:07.033609 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 2.93249 (* 0.0454545 = 0.133295 loss)
I0407 15:10:07.033623 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 2.73401 (* 0.0454545 = 0.124273 loss)
I0407 15:10:07.033637 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.08952 (* 0.0454545 = 0.0949783 loss)
I0407 15:10:07.033650 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.71018 (* 0.0454545 = 0.0777354 loss)
I0407 15:10:07.033663 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 0.757835 (* 0.0454545 = 0.034447 loss)
I0407 15:10:07.033677 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.298781 (* 0.0454545 = 0.0135809 loss)
I0407 15:10:07.033691 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.120029 (* 0.0454545 = 0.00545585 loss)
I0407 15:10:07.033705 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.136168 (* 0.0454545 = 0.00618947 loss)
I0407 15:10:07.033720 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 8.04232e-05 (* 0.0454545 = 3.6556e-06 loss)
I0407 15:10:07.033735 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 7.62358e-05 (* 0.0454545 = 3.46526e-06 loss)
I0407 15:10:07.033748 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 7.20901e-05 (* 0.0454545 = 3.27682e-06 loss)
I0407 15:10:07.033761 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 7.41993e-05 (* 0.0454545 = 3.3727e-06 loss)
I0407 15:10:07.033776 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 7.17847e-05 (* 0.0454545 = 3.26294e-06 loss)
I0407 15:10:07.033789 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 6.73099e-05 (* 0.0454545 = 3.05954e-06 loss)
I0407 15:10:07.033803 32304 solver.cpp:245]     Train net output #38: loss/loss17 = 6.95064e-05 (* 0.0454545 = 3.15938e-06 loss)
I0407 15:10:07.033833 32304 solver.cpp:245]     Train net output #39: loss/loss18 = 7.75378e-05 (* 0.0454545 = 3.52445e-06 loss)
I0407 15:10:07.033849 32304 solver.cpp:245]     Train net output #40: loss/loss19 = 6.89286e-05 (* 0.0454545 = 3.13312e-06 loss)
I0407 15:10:07.033862 32304 solver.cpp:245]     Train net output #41: loss/loss20 = 6.98899e-05 (* 0.0454545 = 3.17681e-06 loss)
I0407 15:10:07.033876 32304 solver.cpp:245]     Train net output #42: loss/loss21 = 6.70992e-05 (* 0.0454545 = 3.04996e-06 loss)
I0407 15:10:07.033890 32304 solver.cpp:245]     Train net output #43: loss/loss22 = 6.48492e-05 (* 0.0454545 = 2.94769e-06 loss)
I0407 15:10:07.033902 32304 solver.cpp:245]     Train net output #44: total_accuracy = 0
I0407 15:10:07.033915 32304 solver.cpp:245]     Train net output #45: total_confidence = 0.00241359
I0407 15:10:07.033926 32304 sgd_solver.cpp:106] Iteration 69000, lr = 0.00862
I0407 15:11:19.843930 32304 solver.cpp:229] Iteration 69500, loss = 0.83685
I0407 15:11:19.844060 32304 solver.cpp:245]     Train net output #0: loss/accuracy01 = 0.3125
I0407 15:11:19.844082 32304 solver.cpp:245]     Train net output #1: loss/accuracy02 = 0.03125
I0407 15:11:19.844096 32304 solver.cpp:245]     Train net output #2: loss/accuracy03 = 0.125
I0407 15:11:19.844110 32304 solver.cpp:245]     Train net output #3: loss/accuracy04 = 0.09375
I0407 15:11:19.844121 32304 solver.cpp:245]     Train net output #4: loss/accuracy05 = 0.375
I0407 15:11:19.844133 32304 solver.cpp:245]     Train net output #5: loss/accuracy06 = 0.5
I0407 15:11:19.844144 32304 solver.cpp:245]     Train net output #6: loss/accuracy07 = 0.75
I0407 15:11:19.844156 32304 solver.cpp:245]     Train net output #7: loss/accuracy08 = 0.9375
I0407 15:11:19.844168 32304 solver.cpp:245]     Train net output #8: loss/accuracy09 = 0.96875
I0407 15:11:19.844180 32304 solver.cpp:245]     Train net output #9: loss/accuracy10 = 1
I0407 15:11:19.844192 32304 solver.cpp:245]     Train net output #10: loss/accuracy11 = 1
I0407 15:11:19.844203 32304 solver.cpp:245]     Train net output #11: loss/accuracy12 = 1
I0407 15:11:19.844214 32304 solver.cpp:245]     Train net output #12: loss/accuracy13 = 1
I0407 15:11:19.844225 32304 solver.cpp:245]     Train net output #13: loss/accuracy14 = 1
I0407 15:11:19.844238 32304 solver.cpp:245]     Train net output #14: loss/accuracy15 = 1
I0407 15:11:19.844249 32304 solver.cpp:245]     Train net output #15: loss/accuracy16 = 1
I0407 15:11:19.844259 32304 solver.cpp:245]     Train net output #16: loss/accuracy17 = 1
I0407 15:11:19.844271 32304 solver.cpp:245]     Train net output #17: loss/accuracy18 = 1
I0407 15:11:19.844282 32304 solver.cpp:245]     Train net output #18: loss/accuracy19 = 1
I0407 15:11:19.844295 32304 solver.cpp:245]     Train net output #19: loss/accuracy20 = 1
I0407 15:11:19.844305 32304 solver.cpp:245]     Train net output #20: loss/accuracy21 = 1
I0407 15:11:19.844316 32304 solver.cpp:245]     Train net output #21: loss/accuracy22 = 1
I0407 15:11:19.844331 32304 solver.cpp:245]     Train net output #22: loss/loss01 = 2.09646 (* 0.0454545 = 0.0952935 loss)
I0407 15:11:19.844347 32304 solver.cpp:245]     Train net output #23: loss/loss02 = 2.86582 (* 0.0454545 = 0.130264 loss)
I0407 15:11:19.844360 32304 solver.cpp:245]     Train net output #24: loss/loss03 = 2.99834 (* 0.0454545 = 0.136288 loss)
I0407 15:11:19.844373 32304 solver.cpp:245]     Train net output #25: loss/loss04 = 3.05693 (* 0.0454545 = 0.138951 loss)
I0407 15:11:19.844388 32304 solver.cpp:245]     Train net output #26: loss/loss05 = 2.38011 (* 0.0454545 = 0.108187 loss)
I0407 15:11:19.844401 32304 solver.cpp:245]     Train net output #27: loss/loss06 = 1.8434 (* 0.0454545 = 0.0837908 loss)
I0407 15:11:19.844415 32304 solver.cpp:245]     Train net output #28: loss/loss07 = 1.05611 (* 0.0454545 = 0.0480052 loss)
I0407 15:11:19.844429 32304 solver.cpp:245]     Train net output #29: loss/loss08 = 0.349453 (* 0.0454545 = 0.0158842 loss)
I0407 15:11:19.844442 32304 solver.cpp:245]     Train net output #30: loss/loss09 = 0.11797 (* 0.0454545 = 0.00536229 loss)
I0407 15:11:19.844456 32304 solver.cpp:245]     Train net output #31: loss/loss10 = 0.0207696 (* 0.0454545 = 0.000944074 loss)
I0407 15:11:19.844470 32304 solver.cpp:245]     Train net output #32: loss/loss11 = 0.000175005 (* 0.0454545 = 7.95477e-06 loss)
I0407 15:11:19.844485 32304 solver.cpp:245]     Train net output #33: loss/loss12 = 0.000198686 (* 0.0454545 = 9.03119e-06 loss)
I0407 15:11:19.844499 32304 solver.cpp:245]     Train net output #34: loss/loss13 = 0.000199518 (* 0.0454545 = 9.06898e-06 loss)
I0407 15:11:19.844513 32304 solver.cpp:245]     Train net output #35: loss/loss14 = 0.00018256 (* 0.0454545 = 8.29819e-06 loss)
I0407 15:11:19.844527 32304 solver.cpp:245]     Train net output #36: loss/loss15 = 0.000181166 (* 0.0454545 = 8.23482e-06 loss)
I0407 15:11:19.844540 32304 solver.cpp:245]     Train net output #37: loss/loss16 = 0.000173913 (* 0.0454545 = 7.90516e-06 loss)
I0407 15:11:19.844554 32304 solver.cpp:245]     Train net output #38: loss/loss17