Last active
April 4, 2016 17:21
-
-
Save stas-sl/8c4b74ffe22b27a995621f86889bcc1b to your computer and use it in GitHub Desktop.
This file has been truncated, but you can view the full file.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| I0404 12:49:59.854396 9252 solver.cpp:280] Solving | |
| I0404 12:49:59.854408 9252 solver.cpp:281] Learning Rate Policy: poly | |
| I0404 12:49:59.914067 9252 solver.cpp:229] Iteration 0, loss = 4.30411 | |
| I0404 12:49:59.914113 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0 | |
| I0404 12:49:59.914134 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 12:49:59.914146 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 12:49:59.914160 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0404 12:49:59.914171 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0 | |
| I0404 12:49:59.914182 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0 | |
| I0404 12:49:59.914193 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0 | |
| I0404 12:49:59.914206 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0 | |
| I0404 12:49:59.914216 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0 | |
| I0404 12:49:59.914228 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0 | |
| I0404 12:49:59.914239 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 0 | |
| I0404 12:49:59.914252 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 0 | |
| I0404 12:49:59.914263 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 0 | |
| I0404 12:49:59.914273 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 0 | |
| I0404 12:49:59.914284 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 0 | |
| I0404 12:49:59.914295 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 0 | |
| I0404 12:49:59.914309 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 0 | |
| I0404 12:49:59.914319 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 0 | |
| I0404 12:49:59.914330 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 0 | |
| I0404 12:49:59.914360 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 0 | |
| I0404 12:49:59.914372 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 0 | |
| I0404 12:49:59.914383 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 0 | |
| I0404 12:49:59.914402 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.30406 (* 0.0454545 = 0.195639 loss) | |
| I0404 12:49:59.914417 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.30407 (* 0.0454545 = 0.19564 loss) | |
| I0404 12:49:59.914432 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.30397 (* 0.0454545 = 0.195635 loss) | |
| I0404 12:49:59.914444 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.30391 (* 0.0454545 = 0.195632 loss) | |
| I0404 12:49:59.914458 9252 solver.cpp:245] Train net output #26: loss/loss05 = 4.3041 (* 0.0454545 = 0.195641 loss) | |
| I0404 12:49:59.914471 9252 solver.cpp:245] Train net output #27: loss/loss06 = 4.30438 (* 0.0454545 = 0.195654 loss) | |
| I0404 12:49:59.914484 9252 solver.cpp:245] Train net output #28: loss/loss07 = 4.30417 (* 0.0454545 = 0.195644 loss) | |
| I0404 12:49:59.914499 9252 solver.cpp:245] Train net output #29: loss/loss08 = 4.3045 (* 0.0454545 = 0.195659 loss) | |
| I0404 12:49:59.914511 9252 solver.cpp:245] Train net output #30: loss/loss09 = 4.30439 (* 0.0454545 = 0.195654 loss) | |
| I0404 12:49:59.914525 9252 solver.cpp:245] Train net output #31: loss/loss10 = 4.3044 (* 0.0454545 = 0.195655 loss) | |
| I0404 12:49:59.914538 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.30437 (* 0.0454545 = 0.195653 loss) | |
| I0404 12:49:59.914552 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.30435 (* 0.0454545 = 0.195652 loss) | |
| I0404 12:49:59.914566 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.30387 (* 0.0454545 = 0.195631 loss) | |
| I0404 12:49:59.914579 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.30365 (* 0.0454545 = 0.19562 loss) | |
| I0404 12:49:59.914593 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.30398 (* 0.0454545 = 0.195636 loss) | |
| I0404 12:49:59.914607 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.30366 (* 0.0454545 = 0.195621 loss) | |
| I0404 12:49:59.914620 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.30445 (* 0.0454545 = 0.195657 loss) | |
| I0404 12:49:59.914634 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.30298 (* 0.0454545 = 0.19559 loss) | |
| I0404 12:49:59.914647 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.30431 (* 0.0454545 = 0.195651 loss) | |
| I0404 12:49:59.914661 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.30423 (* 0.0454545 = 0.195647 loss) | |
| I0404 12:49:59.914674 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.30417 (* 0.0454545 = 0.195644 loss) | |
| I0404 12:49:59.914687 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.30435 (* 0.0454545 = 0.195652 loss) | |
| I0404 12:49:59.914700 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 12:49:59.914710 9252 solver.cpp:245] Train net output #45: total_confidence = 7.80383e-42 | |
| I0404 12:49:59.914736 9252 sgd_solver.cpp:106] Iteration 0, lr = 0.01 | |
| I0404 12:51:07.375053 9252 solver.cpp:229] Iteration 500, loss = 1.7724 | |
| I0404 12:51:07.375259 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0 | |
| I0404 12:51:07.375279 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 12:51:07.375293 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 12:51:07.375305 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0404 12:51:07.375316 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.03125 | |
| I0404 12:51:07.375329 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 12:51:07.375340 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 12:51:07.375352 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 12:51:07.375363 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 12:51:07.375375 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 12:51:07.375386 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 12:51:07.375398 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 12:51:07.375409 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 12:51:07.375421 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 12:51:07.375432 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 12:51:07.375443 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 12:51:07.375454 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 12:51:07.375465 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 12:51:07.375476 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 12:51:07.375488 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 12:51:07.375499 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 12:51:07.375511 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 12:51:07.375526 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.32255 (* 0.0454545 = 0.19648 loss) | |
| I0404 12:51:07.375540 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.09941 (* 0.0454545 = 0.186337 loss) | |
| I0404 12:51:07.375555 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.91037 (* 0.0454545 = 0.177744 loss) | |
| I0404 12:51:07.375568 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.20033 (* 0.0454545 = 0.190924 loss) | |
| I0404 12:51:07.375581 9252 solver.cpp:245] Train net output #26: loss/loss05 = 4.20454 (* 0.0454545 = 0.191115 loss) | |
| I0404 12:51:07.375596 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.02112 (* 0.0454545 = 0.137324 loss) | |
| I0404 12:51:07.375617 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.44311 (* 0.0454545 = 0.065596 loss) | |
| I0404 12:51:07.375634 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.216165 (* 0.0454545 = 0.00982569 loss) | |
| I0404 12:51:07.375651 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.261774 (* 0.0454545 = 0.0118988 loss) | |
| I0404 12:51:07.375665 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0119418 (* 0.0454545 = 0.000542808 loss) | |
| I0404 12:51:07.375690 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000379957 (* 0.0454545 = 1.72708e-05 loss) | |
| I0404 12:51:07.375710 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000385531 (* 0.0454545 = 1.75241e-05 loss) | |
| I0404 12:51:07.375725 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000401627 (* 0.0454545 = 1.82558e-05 loss) | |
| I0404 12:51:07.375741 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000405155 (* 0.0454545 = 1.84162e-05 loss) | |
| I0404 12:51:07.375758 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000403312 (* 0.0454545 = 1.83324e-05 loss) | |
| I0404 12:51:07.375772 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000385303 (* 0.0454545 = 1.75138e-05 loss) | |
| I0404 12:51:07.375785 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000397645 (* 0.0454545 = 1.80748e-05 loss) | |
| I0404 12:51:07.375813 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000401097 (* 0.0454545 = 1.82317e-05 loss) | |
| I0404 12:51:07.375828 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000392782 (* 0.0454545 = 1.78537e-05 loss) | |
| I0404 12:51:07.375843 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000394981 (* 0.0454545 = 1.79537e-05 loss) | |
| I0404 12:51:07.375856 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000393002 (* 0.0454545 = 1.78637e-05 loss) | |
| I0404 12:51:07.375869 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000391555 (* 0.0454545 = 1.7798e-05 loss) | |
| I0404 12:51:07.375881 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 12:51:07.375892 9252 solver.cpp:245] Train net output #45: total_confidence = 1.78324e-07 | |
| I0404 12:51:07.375905 9252 sgd_solver.cpp:106] Iteration 500, lr = 0.009995 | |
| I0404 12:52:15.856787 9252 solver.cpp:229] Iteration 1000, loss = 1.22253 | |
| I0404 12:52:15.856936 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 12:52:15.856959 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 12:52:15.856972 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 12:52:15.856984 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 12:52:15.856997 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0 | |
| I0404 12:52:15.857008 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 12:52:15.857020 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 12:52:15.857033 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 12:52:15.857043 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 12:52:15.857055 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 12:52:15.857066 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 12:52:15.857079 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 12:52:15.857089 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 12:52:15.857101 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 12:52:15.857112 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 12:52:15.857125 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 12:52:15.857136 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 12:52:15.857147 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 12:52:15.857158 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 12:52:15.857170 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 12:52:15.857182 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 12:52:15.857193 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 12:52:15.857208 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.23177 (* 0.0454545 = 0.192353 loss) | |
| I0404 12:52:15.857223 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.23374 (* 0.0454545 = 0.192443 loss) | |
| I0404 12:52:15.857236 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.97236 (* 0.0454545 = 0.180562 loss) | |
| I0404 12:52:15.857250 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.13554 (* 0.0454545 = 0.187979 loss) | |
| I0404 12:52:15.857264 9252 solver.cpp:245] Train net output #26: loss/loss05 = 4.22303 (* 0.0454545 = 0.191956 loss) | |
| I0404 12:52:15.857277 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.85088 (* 0.0454545 = 0.129585 loss) | |
| I0404 12:52:15.857298 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.20312 (* 0.0454545 = 0.0546873 loss) | |
| I0404 12:52:15.857326 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.334296 (* 0.0454545 = 0.0151953 loss) | |
| I0404 12:52:15.857344 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0476095 (* 0.0454545 = 0.00216407 loss) | |
| I0404 12:52:15.857359 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0206874 (* 0.0454545 = 0.000940338 loss) | |
| I0404 12:52:15.857374 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.00108805 (* 0.0454545 = 4.9457e-05 loss) | |
| I0404 12:52:15.857388 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.0011119 (* 0.0454545 = 5.05408e-05 loss) | |
| I0404 12:52:15.857403 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00111138 (* 0.0454545 = 5.05171e-05 loss) | |
| I0404 12:52:15.857445 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.0011269 (* 0.0454545 = 5.12227e-05 loss) | |
| I0404 12:52:15.857475 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.00112522 (* 0.0454545 = 5.11463e-05 loss) | |
| I0404 12:52:15.857492 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.00109794 (* 0.0454545 = 4.99064e-05 loss) | |
| I0404 12:52:15.857507 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.00107797 (* 0.0454545 = 4.89988e-05 loss) | |
| I0404 12:52:15.857539 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.00108679 (* 0.0454545 = 4.93997e-05 loss) | |
| I0404 12:52:15.857555 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00108328 (* 0.0454545 = 4.92399e-05 loss) | |
| I0404 12:52:15.857569 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00108105 (* 0.0454545 = 4.91385e-05 loss) | |
| I0404 12:52:15.857583 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00108393 (* 0.0454545 = 4.92695e-05 loss) | |
| I0404 12:52:15.857597 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.00107318 (* 0.0454545 = 4.87808e-05 loss) | |
| I0404 12:52:15.857609 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 12:52:15.857620 9252 solver.cpp:245] Train net output #45: total_confidence = 3.10054e-08 | |
| I0404 12:52:15.857636 9252 sgd_solver.cpp:106] Iteration 1000, lr = 0.00999 | |
| I0404 12:53:24.246728 9252 solver.cpp:229] Iteration 1500, loss = 1.21602 | |
| I0404 12:53:24.246860 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 12:53:24.246881 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 12:53:24.246894 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 12:53:24.246906 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 12:53:24.246918 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0 | |
| I0404 12:53:24.246932 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 12:53:24.246943 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 12:53:24.246956 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 12:53:24.246968 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 12:53:24.246980 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 12:53:24.246992 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 12:53:24.247004 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 12:53:24.247021 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 12:53:24.247035 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 12:53:24.247046 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 12:53:24.247057 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 12:53:24.247069 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 12:53:24.247081 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 12:53:24.247092 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 12:53:24.247104 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 12:53:24.247115 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 12:53:24.247128 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 12:53:24.247143 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.24159 (* 0.0454545 = 0.1928 loss) | |
| I0404 12:53:24.247158 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.09265 (* 0.0454545 = 0.186029 loss) | |
| I0404 12:53:24.247171 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.14603 (* 0.0454545 = 0.188456 loss) | |
| I0404 12:53:24.247185 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.12971 (* 0.0454545 = 0.187714 loss) | |
| I0404 12:53:24.247198 9252 solver.cpp:245] Train net output #26: loss/loss05 = 4.14565 (* 0.0454545 = 0.188439 loss) | |
| I0404 12:53:24.247212 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.26394 (* 0.0454545 = 0.148361 loss) | |
| I0404 12:53:24.247226 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.87157 (* 0.0454545 = 0.0850714 loss) | |
| I0404 12:53:24.247246 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.673823 (* 0.0454545 = 0.0306283 loss) | |
| I0404 12:53:24.247261 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.486313 (* 0.0454545 = 0.0221051 loss) | |
| I0404 12:53:24.247275 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0185934 (* 0.0454545 = 0.000845155 loss) | |
| I0404 12:53:24.247289 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000672782 (* 0.0454545 = 3.0581e-05 loss) | |
| I0404 12:53:24.247303 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000680713 (* 0.0454545 = 3.09415e-05 loss) | |
| I0404 12:53:24.247318 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00068355 (* 0.0454545 = 3.10704e-05 loss) | |
| I0404 12:53:24.247331 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000686861 (* 0.0454545 = 3.12209e-05 loss) | |
| I0404 12:53:24.247345 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000685819 (* 0.0454545 = 3.11736e-05 loss) | |
| I0404 12:53:24.247359 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000677214 (* 0.0454545 = 3.07825e-05 loss) | |
| I0404 12:53:24.247375 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000669081 (* 0.0454545 = 3.04128e-05 loss) | |
| I0404 12:53:24.247401 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.00066785 (* 0.0454545 = 3.03568e-05 loss) | |
| I0404 12:53:24.247421 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000670157 (* 0.0454545 = 3.04617e-05 loss) | |
| I0404 12:53:24.247442 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000668844 (* 0.0454545 = 3.0402e-05 loss) | |
| I0404 12:53:24.247473 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000674554 (* 0.0454545 = 3.06616e-05 loss) | |
| I0404 12:53:24.247489 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000669536 (* 0.0454545 = 3.04335e-05 loss) | |
| I0404 12:53:24.247501 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 12:53:24.247514 9252 solver.cpp:245] Train net output #45: total_confidence = 6.45663e-08 | |
| I0404 12:53:24.247527 9252 sgd_solver.cpp:106] Iteration 1500, lr = 0.009985 | |
| I0404 12:54:33.607766 9252 solver.cpp:229] Iteration 2000, loss = 1.20272 | |
| I0404 12:54:33.607903 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 12:54:33.607923 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 12:54:33.607936 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 12:54:33.607949 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 12:54:33.607960 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.03125 | |
| I0404 12:54:33.607972 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 12:54:33.607985 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 12:54:33.607996 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 1 | |
| I0404 12:54:33.608008 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 12:54:33.608021 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 12:54:33.608031 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 12:54:33.608043 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 12:54:33.608054 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 12:54:33.608067 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 12:54:33.608078 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 12:54:33.608088 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 12:54:33.608100 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 12:54:33.608111 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 12:54:33.608124 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 12:54:33.608134 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 12:54:33.608145 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 12:54:33.608157 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 12:54:33.608172 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.2188 (* 0.0454545 = 0.191764 loss) | |
| I0404 12:54:33.608187 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.41661 (* 0.0454545 = 0.200755 loss) | |
| I0404 12:54:33.608201 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.82629 (* 0.0454545 = 0.173922 loss) | |
| I0404 12:54:33.608214 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.09728 (* 0.0454545 = 0.18624 loss) | |
| I0404 12:54:33.608228 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.97638 (* 0.0454545 = 0.180744 loss) | |
| I0404 12:54:33.608242 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.71667 (* 0.0454545 = 0.123485 loss) | |
| I0404 12:54:33.608255 9252 solver.cpp:245] Train net output #28: loss/loss07 = 2.23387 (* 0.0454545 = 0.10154 loss) | |
| I0404 12:54:33.608269 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.0508615 (* 0.0454545 = 0.00231189 loss) | |
| I0404 12:54:33.608283 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0129023 (* 0.0454545 = 0.000586467 loss) | |
| I0404 12:54:33.608297 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00555801 (* 0.0454545 = 0.000252637 loss) | |
| I0404 12:54:33.608312 9252 solver.cpp:245] Train net output #32: loss/loss11 = 9.61698e-05 (* 0.0454545 = 4.37135e-06 loss) | |
| I0404 12:54:33.608326 9252 solver.cpp:245] Train net output #33: loss/loss12 = 9.74535e-05 (* 0.0454545 = 4.4297e-06 loss) | |
| I0404 12:54:33.608340 9252 solver.cpp:245] Train net output #34: loss/loss13 = 9.65834e-05 (* 0.0454545 = 4.39015e-06 loss) | |
| I0404 12:54:33.608353 9252 solver.cpp:245] Train net output #35: loss/loss14 = 9.81578e-05 (* 0.0454545 = 4.46172e-06 loss) | |
| I0404 12:54:33.608367 9252 solver.cpp:245] Train net output #36: loss/loss15 = 9.78932e-05 (* 0.0454545 = 4.44969e-06 loss) | |
| I0404 12:54:33.608381 9252 solver.cpp:245] Train net output #37: loss/loss16 = 9.73119e-05 (* 0.0454545 = 4.42327e-06 loss) | |
| I0404 12:54:33.608395 9252 solver.cpp:245] Train net output #38: loss/loss17 = 9.61027e-05 (* 0.0454545 = 4.3683e-06 loss) | |
| I0404 12:54:33.608425 9252 solver.cpp:245] Train net output #39: loss/loss18 = 9.59219e-05 (* 0.0454545 = 4.36009e-06 loss) | |
| I0404 12:54:33.608441 9252 solver.cpp:245] Train net output #40: loss/loss19 = 9.62815e-05 (* 0.0454545 = 4.37643e-06 loss) | |
| I0404 12:54:33.608455 9252 solver.cpp:245] Train net output #41: loss/loss20 = 9.63002e-05 (* 0.0454545 = 4.37728e-06 loss) | |
| I0404 12:54:33.608469 9252 solver.cpp:245] Train net output #42: loss/loss21 = 9.58847e-05 (* 0.0454545 = 4.35839e-06 loss) | |
| I0404 12:54:33.608482 9252 solver.cpp:245] Train net output #43: loss/loss22 = 9.61176e-05 (* 0.0454545 = 4.36898e-06 loss) | |
| I0404 12:54:33.608494 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 12:54:33.608505 9252 solver.cpp:245] Train net output #45: total_confidence = 6.39708e-07 | |
| I0404 12:54:33.608518 9252 sgd_solver.cpp:106] Iteration 2000, lr = 0.00998 | |
| I0404 12:55:42.038975 9252 solver.cpp:229] Iteration 2500, loss = 1.20067 | |
| I0404 12:55:42.039109 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 12:55:42.039129 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 12:55:42.039142 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 12:55:42.039155 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 12:55:42.039166 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.09375 | |
| I0404 12:55:42.039178 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 12:55:42.039191 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 12:55:42.039202 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.78125 | |
| I0404 12:55:42.039214 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.875 | |
| I0404 12:55:42.039227 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.90625 | |
| I0404 12:55:42.039237 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 12:55:42.039249 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 12:55:42.039261 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 12:55:42.039273 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 12:55:42.039284 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 12:55:42.039295 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 12:55:42.039307 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 12:55:42.039319 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 12:55:42.039330 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 12:55:42.039341 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 12:55:42.039353 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 12:55:42.039366 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 12:55:42.039388 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.12703 (* 0.0454545 = 0.187592 loss) | |
| I0404 12:55:42.039404 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.32417 (* 0.0454545 = 0.196553 loss) | |
| I0404 12:55:42.039418 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.1215 (* 0.0454545 = 0.187341 loss) | |
| I0404 12:55:42.039432 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.21418 (* 0.0454545 = 0.191554 loss) | |
| I0404 12:55:42.039445 9252 solver.cpp:245] Train net output #26: loss/loss05 = 4.03017 (* 0.0454545 = 0.183189 loss) | |
| I0404 12:55:42.039459 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.02613 (* 0.0454545 = 0.137552 loss) | |
| I0404 12:55:42.039474 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.78655 (* 0.0454545 = 0.0812069 loss) | |
| I0404 12:55:42.039486 9252 solver.cpp:245] Train net output #29: loss/loss08 = 1.33705 (* 0.0454545 = 0.0607748 loss) | |
| I0404 12:55:42.039500 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.945743 (* 0.0454545 = 0.0429883 loss) | |
| I0404 12:55:42.039515 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.649315 (* 0.0454545 = 0.0295143 loss) | |
| I0404 12:55:42.039528 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.0044619 (* 0.0454545 = 0.000202814 loss) | |
| I0404 12:55:42.039542 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.00450497 (* 0.0454545 = 0.000204771 loss) | |
| I0404 12:55:42.039556 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00446261 (* 0.0454545 = 0.000202846 loss) | |
| I0404 12:55:42.039571 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.00448405 (* 0.0454545 = 0.000203821 loss) | |
| I0404 12:55:42.039584 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.00447214 (* 0.0454545 = 0.000203279 loss) | |
| I0404 12:55:42.039598 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.00447301 (* 0.0454545 = 0.000203318 loss) | |
| I0404 12:55:42.039613 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.00445185 (* 0.0454545 = 0.000202357 loss) | |
| I0404 12:55:42.039644 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.00444163 (* 0.0454545 = 0.000201892 loss) | |
| I0404 12:55:42.039659 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00447151 (* 0.0454545 = 0.00020325 loss) | |
| I0404 12:55:42.039674 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00444819 (* 0.0454545 = 0.000202191 loss) | |
| I0404 12:55:42.039687 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00445556 (* 0.0454545 = 0.000202526 loss) | |
| I0404 12:55:42.039701 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.00446083 (* 0.0454545 = 0.000202765 loss) | |
| I0404 12:55:42.039713 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 12:55:42.039724 9252 solver.cpp:245] Train net output #45: total_confidence = 1.27741e-07 | |
| I0404 12:55:42.039738 9252 sgd_solver.cpp:106] Iteration 2500, lr = 0.009975 | |
| I0404 12:56:50.566354 9252 solver.cpp:229] Iteration 3000, loss = 1.1954 | |
| I0404 12:56:50.566473 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 12:56:50.566493 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 12:56:50.566506 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 12:56:50.566519 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 12:56:50.566534 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 12:56:50.566556 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 12:56:50.566576 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 12:56:50.566589 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 12:56:50.566601 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 12:56:50.566613 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 12:56:50.566624 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 12:56:50.566637 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 12:56:50.566648 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 12:56:50.566658 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 12:56:50.566669 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 12:56:50.566681 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 12:56:50.566692 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 12:56:50.566704 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 12:56:50.566715 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 12:56:50.566726 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 12:56:50.566737 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 12:56:50.566750 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 12:56:50.566764 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.19172 (* 0.0454545 = 0.190533 loss) | |
| I0404 12:56:50.566779 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.08727 (* 0.0454545 = 0.185785 loss) | |
| I0404 12:56:50.566792 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.02921 (* 0.0454545 = 0.183146 loss) | |
| I0404 12:56:50.566807 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.14916 (* 0.0454545 = 0.188598 loss) | |
| I0404 12:56:50.566820 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.84619 (* 0.0454545 = 0.174827 loss) | |
| I0404 12:56:50.566833 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.62891 (* 0.0454545 = 0.119496 loss) | |
| I0404 12:56:50.566848 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.46014 (* 0.0454545 = 0.06637 loss) | |
| I0404 12:56:50.566860 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.900181 (* 0.0454545 = 0.0409173 loss) | |
| I0404 12:56:50.566874 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.226947 (* 0.0454545 = 0.0103158 loss) | |
| I0404 12:56:50.566889 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0113587 (* 0.0454545 = 0.000516303 loss) | |
| I0404 12:56:50.566905 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000321962 (* 0.0454545 = 1.46346e-05 loss) | |
| I0404 12:56:50.566920 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000323182 (* 0.0454545 = 1.46901e-05 loss) | |
| I0404 12:56:50.566934 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000323084 (* 0.0454545 = 1.46857e-05 loss) | |
| I0404 12:56:50.566947 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000322976 (* 0.0454545 = 1.46807e-05 loss) | |
| I0404 12:56:50.566962 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.00032298 (* 0.0454545 = 1.46809e-05 loss) | |
| I0404 12:56:50.566975 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000324606 (* 0.0454545 = 1.47548e-05 loss) | |
| I0404 12:56:50.566989 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000319778 (* 0.0454545 = 1.45354e-05 loss) | |
| I0404 12:56:50.567019 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000320537 (* 0.0454545 = 1.45699e-05 loss) | |
| I0404 12:56:50.567035 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000321561 (* 0.0454545 = 1.46164e-05 loss) | |
| I0404 12:56:50.567049 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00031967 (* 0.0454545 = 1.45305e-05 loss) | |
| I0404 12:56:50.567062 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000321846 (* 0.0454545 = 1.46294e-05 loss) | |
| I0404 12:56:50.567075 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000319937 (* 0.0454545 = 1.45426e-05 loss) | |
| I0404 12:56:50.567087 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 12:56:50.567100 9252 solver.cpp:245] Train net output #45: total_confidence = 1.43543e-07 | |
| I0404 12:56:50.567112 9252 sgd_solver.cpp:106] Iteration 3000, lr = 0.00997 | |
| I0404 12:57:59.391693 9252 solver.cpp:229] Iteration 3500, loss = 1.18162 | |
| I0404 12:57:59.391849 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 12:57:59.391870 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 12:57:59.391883 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 12:57:59.391896 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 12:57:59.391907 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 12:57:59.391919 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 12:57:59.391932 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.65625 | |
| I0404 12:57:59.391943 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 12:57:59.391955 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 12:57:59.391968 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 12:57:59.391979 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 12:57:59.391993 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 12:57:59.392004 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 12:57:59.392014 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 12:57:59.392026 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 12:57:59.392037 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 12:57:59.392050 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 12:57:59.392060 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 12:57:59.392071 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 12:57:59.392083 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 12:57:59.392094 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 12:57:59.392107 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 12:57:59.392122 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.05716 (* 0.0454545 = 0.184416 loss) | |
| I0404 12:57:59.392137 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.05274 (* 0.0454545 = 0.184215 loss) | |
| I0404 12:57:59.392150 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.81753 (* 0.0454545 = 0.173524 loss) | |
| I0404 12:57:59.392164 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.06611 (* 0.0454545 = 0.184823 loss) | |
| I0404 12:57:59.392177 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.81977 (* 0.0454545 = 0.173626 loss) | |
| I0404 12:57:59.392191 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.97013 (* 0.0454545 = 0.135006 loss) | |
| I0404 12:57:59.392204 9252 solver.cpp:245] Train net output #28: loss/loss07 = 2.01672 (* 0.0454545 = 0.091669 loss) | |
| I0404 12:57:59.392218 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.45799 (* 0.0454545 = 0.0208177 loss) | |
| I0404 12:57:59.392232 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.42364 (* 0.0454545 = 0.0192564 loss) | |
| I0404 12:57:59.392246 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.229441 (* 0.0454545 = 0.0104291 loss) | |
| I0404 12:57:59.392261 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000752241 (* 0.0454545 = 3.41928e-05 loss) | |
| I0404 12:57:59.392274 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000761775 (* 0.0454545 = 3.46261e-05 loss) | |
| I0404 12:57:59.392287 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000752756 (* 0.0454545 = 3.42162e-05 loss) | |
| I0404 12:57:59.392302 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000754407 (* 0.0454545 = 3.42912e-05 loss) | |
| I0404 12:57:59.392315 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000755757 (* 0.0454545 = 3.43526e-05 loss) | |
| I0404 12:57:59.392329 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000754994 (* 0.0454545 = 3.43179e-05 loss) | |
| I0404 12:57:59.392343 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000751246 (* 0.0454545 = 3.41476e-05 loss) | |
| I0404 12:57:59.392370 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000751763 (* 0.0454545 = 3.4171e-05 loss) | |
| I0404 12:57:59.392386 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000754765 (* 0.0454545 = 3.43075e-05 loss) | |
| I0404 12:57:59.392413 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000749751 (* 0.0454545 = 3.40796e-05 loss) | |
| I0404 12:57:59.392428 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000752682 (* 0.0454545 = 3.42128e-05 loss) | |
| I0404 12:57:59.392442 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000752349 (* 0.0454545 = 3.41977e-05 loss) | |
| I0404 12:57:59.392454 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 12:57:59.392467 9252 solver.cpp:245] Train net output #45: total_confidence = 2.74334e-07 | |
| I0404 12:57:59.392482 9252 sgd_solver.cpp:106] Iteration 3500, lr = 0.009965 | |
| I0404 12:59:08.836720 9252 solver.cpp:229] Iteration 4000, loss = 1.18269 | |
| I0404 12:59:08.836853 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 12:59:08.836874 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 12:59:08.836887 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 12:59:08.836899 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 12:59:08.836911 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 12:59:08.836923 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 12:59:08.836935 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 12:59:08.836946 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 12:59:08.836959 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 12:59:08.836971 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 12:59:08.836982 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 12:59:08.836993 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 12:59:08.837005 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 12:59:08.837016 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 12:59:08.837028 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 12:59:08.837039 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 12:59:08.837051 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 12:59:08.837062 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 12:59:08.837074 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 12:59:08.837085 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 12:59:08.837096 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 12:59:08.837107 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 12:59:08.837122 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.11616 (* 0.0454545 = 0.187098 loss) | |
| I0404 12:59:08.837137 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.2007 (* 0.0454545 = 0.190941 loss) | |
| I0404 12:59:08.837151 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.06892 (* 0.0454545 = 0.184951 loss) | |
| I0404 12:59:08.837164 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.08144 (* 0.0454545 = 0.18552 loss) | |
| I0404 12:59:08.837178 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.77917 (* 0.0454545 = 0.171781 loss) | |
| I0404 12:59:08.837191 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.51692 (* 0.0454545 = 0.114406 loss) | |
| I0404 12:59:08.837205 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.09516 (* 0.0454545 = 0.0497801 loss) | |
| I0404 12:59:08.837219 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.472731 (* 0.0454545 = 0.0214878 loss) | |
| I0404 12:59:08.837234 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.495045 (* 0.0454545 = 0.0225021 loss) | |
| I0404 12:59:08.837247 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.1985 (* 0.0454545 = 0.00902272 loss) | |
| I0404 12:59:08.837261 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000513174 (* 0.0454545 = 2.33261e-05 loss) | |
| I0404 12:59:08.837276 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000516585 (* 0.0454545 = 2.34811e-05 loss) | |
| I0404 12:59:08.837290 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000512928 (* 0.0454545 = 2.33149e-05 loss) | |
| I0404 12:59:08.837303 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000510892 (* 0.0454545 = 2.32224e-05 loss) | |
| I0404 12:59:08.837317 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000510448 (* 0.0454545 = 2.32022e-05 loss) | |
| I0404 12:59:08.837332 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000512603 (* 0.0454545 = 2.33001e-05 loss) | |
| I0404 12:59:08.837345 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.00051136 (* 0.0454545 = 2.32436e-05 loss) | |
| I0404 12:59:08.837375 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000516205 (* 0.0454545 = 2.34638e-05 loss) | |
| I0404 12:59:08.837390 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000512909 (* 0.0454545 = 2.3314e-05 loss) | |
| I0404 12:59:08.837405 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000512536 (* 0.0454545 = 2.32971e-05 loss) | |
| I0404 12:59:08.837436 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000512079 (* 0.0454545 = 2.32763e-05 loss) | |
| I0404 12:59:08.837455 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000514801 (* 0.0454545 = 2.34001e-05 loss) | |
| I0404 12:59:08.837466 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 12:59:08.837478 9252 solver.cpp:245] Train net output #45: total_confidence = 2.75815e-07 | |
| I0404 12:59:08.837492 9252 sgd_solver.cpp:106] Iteration 4000, lr = 0.00996 | |
| I0404 13:00:17.918923 9252 solver.cpp:229] Iteration 4500, loss = 1.18205 | |
| I0404 13:00:17.919051 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:00:17.919072 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:00:17.919085 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:00:17.919097 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0404 13:00:17.919109 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 13:00:17.919121 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 13:00:17.919134 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 13:00:17.919145 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:00:17.919157 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:00:17.919168 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:00:17.919180 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:00:17.919191 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:00:17.919203 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:00:17.919214 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:00:17.919225 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:00:17.919237 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:00:17.919248 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:00:17.919260 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:00:17.919271 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:00:17.919283 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:00:17.919294 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:00:17.919306 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:00:17.919322 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.1285 (* 0.0454545 = 0.187659 loss) | |
| I0404 13:00:17.919335 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.1998 (* 0.0454545 = 0.1909 loss) | |
| I0404 13:00:17.919349 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.86007 (* 0.0454545 = 0.175458 loss) | |
| I0404 13:00:17.919363 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.11513 (* 0.0454545 = 0.187052 loss) | |
| I0404 13:00:17.919378 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.83896 (* 0.0454545 = 0.174498 loss) | |
| I0404 13:00:17.919391 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.65069 (* 0.0454545 = 0.120486 loss) | |
| I0404 13:00:17.919405 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.34558 (* 0.0454545 = 0.0611626 loss) | |
| I0404 13:00:17.919419 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.52358 (* 0.0454545 = 0.0237991 loss) | |
| I0404 13:00:17.919432 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.22212 (* 0.0454545 = 0.0100963 loss) | |
| I0404 13:00:17.919446 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0290877 (* 0.0454545 = 0.00132217 loss) | |
| I0404 13:00:17.919461 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000886207 (* 0.0454545 = 4.02821e-05 loss) | |
| I0404 13:00:17.919476 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.00088845 (* 0.0454545 = 4.03841e-05 loss) | |
| I0404 13:00:17.919489 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000883839 (* 0.0454545 = 4.01745e-05 loss) | |
| I0404 13:00:17.919503 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000884753 (* 0.0454545 = 4.0216e-05 loss) | |
| I0404 13:00:17.919517 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000884085 (* 0.0454545 = 4.01857e-05 loss) | |
| I0404 13:00:17.919531 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000885506 (* 0.0454545 = 4.02503e-05 loss) | |
| I0404 13:00:17.919544 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000877949 (* 0.0454545 = 3.99068e-05 loss) | |
| I0404 13:00:17.919576 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000886477 (* 0.0454545 = 4.02944e-05 loss) | |
| I0404 13:00:17.919591 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000883769 (* 0.0454545 = 4.01713e-05 loss) | |
| I0404 13:00:17.919605 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000885265 (* 0.0454545 = 4.02393e-05 loss) | |
| I0404 13:00:17.919620 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000884296 (* 0.0454545 = 4.01953e-05 loss) | |
| I0404 13:00:17.919633 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000886551 (* 0.0454545 = 4.02978e-05 loss) | |
| I0404 13:00:17.919646 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:00:17.919656 9252 solver.cpp:245] Train net output #45: total_confidence = 1.0924e-07 | |
| I0404 13:00:17.919669 9252 sgd_solver.cpp:106] Iteration 4500, lr = 0.009955 | |
| I0404 13:01:27.153278 9252 solver.cpp:229] Iteration 5000, loss = 1.17493 | |
| I0404 13:01:27.153406 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0 | |
| I0404 13:01:27.153439 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:01:27.153452 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 13:01:27.153465 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:01:27.153476 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 13:01:27.153488 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 13:01:27.153501 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 13:01:27.153512 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:01:27.153523 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:01:27.153535 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:01:27.153547 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:01:27.153558 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:01:27.153569 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:01:27.153580 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:01:27.153591 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:01:27.153604 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:01:27.153614 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:01:27.153625 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:01:27.153637 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:01:27.153648 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:01:27.153661 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:01:27.153671 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:01:27.153687 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.05966 (* 0.0454545 = 0.18453 loss) | |
| I0404 13:01:27.153702 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.13274 (* 0.0454545 = 0.187852 loss) | |
| I0404 13:01:27.153715 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.05962 (* 0.0454545 = 0.184528 loss) | |
| I0404 13:01:27.153729 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.13526 (* 0.0454545 = 0.187966 loss) | |
| I0404 13:01:27.153743 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.92443 (* 0.0454545 = 0.178383 loss) | |
| I0404 13:01:27.153756 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.12256 (* 0.0454545 = 0.141935 loss) | |
| I0404 13:01:27.153769 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.77526 (* 0.0454545 = 0.0806936 loss) | |
| I0404 13:01:27.153782 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.44313 (* 0.0454545 = 0.0201423 loss) | |
| I0404 13:01:27.153796 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0777645 (* 0.0454545 = 0.00353475 loss) | |
| I0404 13:01:27.153810 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0307648 (* 0.0454545 = 0.0013984 loss) | |
| I0404 13:01:27.153825 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.00121718 (* 0.0454545 = 5.53262e-05 loss) | |
| I0404 13:01:27.153839 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.00121657 (* 0.0454545 = 5.52987e-05 loss) | |
| I0404 13:01:27.153853 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00121172 (* 0.0454545 = 5.50782e-05 loss) | |
| I0404 13:01:27.153867 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.00121452 (* 0.0454545 = 5.52056e-05 loss) | |
| I0404 13:01:27.153880 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.00120842 (* 0.0454545 = 5.49283e-05 loss) | |
| I0404 13:01:27.153894 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.00121334 (* 0.0454545 = 5.51516e-05 loss) | |
| I0404 13:01:27.153911 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.0012029 (* 0.0454545 = 5.46773e-05 loss) | |
| I0404 13:01:27.153944 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.00121289 (* 0.0454545 = 5.51312e-05 loss) | |
| I0404 13:01:27.153959 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00120928 (* 0.0454545 = 5.49673e-05 loss) | |
| I0404 13:01:27.153972 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00121729 (* 0.0454545 = 5.53312e-05 loss) | |
| I0404 13:01:27.153986 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00121115 (* 0.0454545 = 5.50524e-05 loss) | |
| I0404 13:01:27.154000 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.0012157 (* 0.0454545 = 5.52592e-05 loss) | |
| I0404 13:01:27.154012 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:01:27.154023 9252 solver.cpp:245] Train net output #45: total_confidence = 1.37481e-07 | |
| I0404 13:01:27.154037 9252 sgd_solver.cpp:106] Iteration 5000, lr = 0.00995 | |
| I0404 13:02:36.922932 9252 solver.cpp:229] Iteration 5500, loss = 1.17123 | |
| I0404 13:02:36.923130 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:02:36.923152 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:02:36.923166 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:02:36.923177 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 13:02:36.923189 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 13:02:36.923202 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 13:02:36.923213 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 13:02:36.923224 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:02:36.923236 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:02:36.923249 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:02:36.923259 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:02:36.923271 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:02:36.923282 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:02:36.923295 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:02:36.923305 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:02:36.923317 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:02:36.923329 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:02:36.923341 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:02:36.923352 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:02:36.923362 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:02:36.923374 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:02:36.923385 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:02:36.923400 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.28086 (* 0.0454545 = 0.194585 loss) | |
| I0404 13:02:36.923415 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.02756 (* 0.0454545 = 0.183071 loss) | |
| I0404 13:02:36.923429 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.05606 (* 0.0454545 = 0.184366 loss) | |
| I0404 13:02:36.923442 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.03525 (* 0.0454545 = 0.18342 loss) | |
| I0404 13:02:36.923455 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.70728 (* 0.0454545 = 0.168513 loss) | |
| I0404 13:02:36.923470 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.6152 (* 0.0454545 = 0.118873 loss) | |
| I0404 13:02:36.923483 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.30571 (* 0.0454545 = 0.0593504 loss) | |
| I0404 13:02:36.923496 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.385719 (* 0.0454545 = 0.0175327 loss) | |
| I0404 13:02:36.923517 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.426645 (* 0.0454545 = 0.019393 loss) | |
| I0404 13:02:36.923547 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.257499 (* 0.0454545 = 0.0117045 loss) | |
| I0404 13:02:36.923578 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000393137 (* 0.0454545 = 1.78699e-05 loss) | |
| I0404 13:02:36.923625 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000393381 (* 0.0454545 = 1.7881e-05 loss) | |
| I0404 13:02:36.923648 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000391402 (* 0.0454545 = 1.7791e-05 loss) | |
| I0404 13:02:36.923662 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000394458 (* 0.0454545 = 1.79299e-05 loss) | |
| I0404 13:02:36.923677 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000392483 (* 0.0454545 = 1.78401e-05 loss) | |
| I0404 13:02:36.923691 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000391103 (* 0.0454545 = 1.77774e-05 loss) | |
| I0404 13:02:36.923705 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000390916 (* 0.0454545 = 1.77689e-05 loss) | |
| I0404 13:02:36.923734 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000392852 (* 0.0454545 = 1.78569e-05 loss) | |
| I0404 13:02:36.923753 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000391649 (* 0.0454545 = 1.78022e-05 loss) | |
| I0404 13:02:36.923768 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000392922 (* 0.0454545 = 1.78601e-05 loss) | |
| I0404 13:02:36.923781 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000392244 (* 0.0454545 = 1.78293e-05 loss) | |
| I0404 13:02:36.923794 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000393735 (* 0.0454545 = 1.7897e-05 loss) | |
| I0404 13:02:36.923806 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:02:36.923817 9252 solver.cpp:245] Train net output #45: total_confidence = 2.83792e-07 | |
| I0404 13:02:36.923833 9252 sgd_solver.cpp:106] Iteration 5500, lr = 0.009945 | |
| I0404 13:03:46.285241 9252 solver.cpp:229] Iteration 6000, loss = 1.1733 | |
| I0404 13:03:46.285365 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:03:46.285387 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:03:46.285399 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:03:46.285411 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 13:03:46.285423 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 13:03:46.285436 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 13:03:46.285449 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:03:46.285460 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:03:46.285471 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:03:46.285500 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:03:46.285514 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:03:46.285526 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:03:46.285538 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:03:46.285549 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:03:46.285562 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:03:46.285573 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:03:46.285584 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:03:46.285596 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:03:46.285607 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:03:46.285619 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:03:46.285630 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:03:46.285641 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:03:46.285657 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.02091 (* 0.0454545 = 0.182769 loss) | |
| I0404 13:03:46.285671 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.0891 (* 0.0454545 = 0.185868 loss) | |
| I0404 13:03:46.285686 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.8803 (* 0.0454545 = 0.176377 loss) | |
| I0404 13:03:46.285699 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.01181 (* 0.0454545 = 0.182355 loss) | |
| I0404 13:03:46.285712 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.69245 (* 0.0454545 = 0.167839 loss) | |
| I0404 13:03:46.285727 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.14962 (* 0.0454545 = 0.09771 loss) | |
| I0404 13:03:46.285740 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.57003 (* 0.0454545 = 0.0713652 loss) | |
| I0404 13:03:46.285758 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.584002 (* 0.0454545 = 0.0265455 loss) | |
| I0404 13:03:46.285771 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.361665 (* 0.0454545 = 0.0164393 loss) | |
| I0404 13:03:46.285784 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.22523 (* 0.0454545 = 0.0102377 loss) | |
| I0404 13:03:46.285799 9252 solver.cpp:245] Train net output #32: loss/loss11 = 7.10326e-05 (* 0.0454545 = 3.22875e-06 loss) | |
| I0404 13:03:46.285814 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.12208e-05 (* 0.0454545 = 3.23731e-06 loss) | |
| I0404 13:03:46.285827 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.09729e-05 (* 0.0454545 = 3.22604e-06 loss) | |
| I0404 13:03:46.285841 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.10176e-05 (* 0.0454545 = 3.22807e-06 loss) | |
| I0404 13:03:46.285856 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.06785e-05 (* 0.0454545 = 3.21266e-06 loss) | |
| I0404 13:03:46.285869 9252 solver.cpp:245] Train net output #37: loss/loss16 = 7.10549e-05 (* 0.0454545 = 3.22977e-06 loss) | |
| I0404 13:03:46.285883 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.07531e-05 (* 0.0454545 = 3.21605e-06 loss) | |
| I0404 13:03:46.285914 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.15748e-05 (* 0.0454545 = 3.2534e-06 loss) | |
| I0404 13:03:46.285929 9252 solver.cpp:245] Train net output #40: loss/loss19 = 7.07493e-05 (* 0.0454545 = 3.21588e-06 loss) | |
| I0404 13:03:46.285943 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.12487e-05 (* 0.0454545 = 3.23858e-06 loss) | |
| I0404 13:03:46.285958 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.10549e-05 (* 0.0454545 = 3.22977e-06 loss) | |
| I0404 13:03:46.285971 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.09618e-05 (* 0.0454545 = 3.22553e-06 loss) | |
| I0404 13:03:46.285984 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:03:46.285995 9252 solver.cpp:245] Train net output #45: total_confidence = 4.6566e-07 | |
| I0404 13:03:46.286007 9252 sgd_solver.cpp:106] Iteration 6000, lr = 0.00994 | |
| I0404 13:04:55.502306 9252 solver.cpp:229] Iteration 6500, loss = 1.16481 | |
| I0404 13:04:55.502413 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 13:04:55.502432 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:04:55.502445 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 13:04:55.502457 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 13:04:55.502470 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 13:04:55.502480 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 13:04:55.502492 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:04:55.502506 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:04:55.502516 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:04:55.502528 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:04:55.502539 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:04:55.502552 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:04:55.502562 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:04:55.502574 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:04:55.502585 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:04:55.502598 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:04:55.502609 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:04:55.502620 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:04:55.502631 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:04:55.502642 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:04:55.502655 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:04:55.502665 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:04:55.502681 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.95483 (* 0.0454545 = 0.179765 loss) | |
| I0404 13:04:55.502696 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.05699 (* 0.0454545 = 0.184408 loss) | |
| I0404 13:04:55.502710 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.8838 (* 0.0454545 = 0.176536 loss) | |
| I0404 13:04:55.502723 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.09547 (* 0.0454545 = 0.186158 loss) | |
| I0404 13:04:55.502737 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.78214 (* 0.0454545 = 0.171916 loss) | |
| I0404 13:04:55.502754 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.05817 (* 0.0454545 = 0.139008 loss) | |
| I0404 13:04:55.502768 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.72657 (* 0.0454545 = 0.0784806 loss) | |
| I0404 13:04:55.502781 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.667593 (* 0.0454545 = 0.0303451 loss) | |
| I0404 13:04:55.502795 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.431967 (* 0.0454545 = 0.0196349 loss) | |
| I0404 13:04:55.502810 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.252548 (* 0.0454545 = 0.0114794 loss) | |
| I0404 13:04:55.502823 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.00186272 (* 0.0454545 = 8.4669e-05 loss) | |
| I0404 13:04:55.502837 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.00187167 (* 0.0454545 = 8.50758e-05 loss) | |
| I0404 13:04:55.502851 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00185161 (* 0.0454545 = 8.41642e-05 loss) | |
| I0404 13:04:55.502866 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.00185635 (* 0.0454545 = 8.43795e-05 loss) | |
| I0404 13:04:55.502879 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.00185769 (* 0.0454545 = 8.44405e-05 loss) | |
| I0404 13:04:55.502893 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.00185773 (* 0.0454545 = 8.44421e-05 loss) | |
| I0404 13:04:55.502907 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.0018572 (* 0.0454545 = 8.44181e-05 loss) | |
| I0404 13:04:55.502938 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.00186652 (* 0.0454545 = 8.48416e-05 loss) | |
| I0404 13:04:55.502954 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00185567 (* 0.0454545 = 8.43487e-05 loss) | |
| I0404 13:04:55.502969 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00186178 (* 0.0454545 = 8.46266e-05 loss) | |
| I0404 13:04:55.502982 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00185334 (* 0.0454545 = 8.42427e-05 loss) | |
| I0404 13:04:55.502996 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.00186202 (* 0.0454545 = 8.46372e-05 loss) | |
| I0404 13:04:55.503008 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:04:55.503020 9252 solver.cpp:245] Train net output #45: total_confidence = 1.74413e-07 | |
| I0404 13:04:55.503033 9252 sgd_solver.cpp:106] Iteration 6500, lr = 0.009935 | |
| I0404 13:06:04.937978 9252 solver.cpp:229] Iteration 7000, loss = 1.16316 | |
| I0404 13:06:04.938092 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:06:04.938112 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:06:04.938124 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:06:04.938136 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0404 13:06:04.938148 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 13:06:04.938160 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 13:06:04.938172 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 13:06:04.938184 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 13:06:04.938195 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:06:04.938207 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:06:04.938218 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:06:04.938230 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:06:04.938241 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:06:04.938252 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:06:04.938264 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:06:04.938276 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:06:04.938287 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:06:04.938298 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:06:04.938309 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:06:04.938321 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:06:04.938333 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:06:04.938344 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:06:04.938362 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.0929 (* 0.0454545 = 0.186041 loss) | |
| I0404 13:06:04.938386 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.09424 (* 0.0454545 = 0.186102 loss) | |
| I0404 13:06:04.938401 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.08627 (* 0.0454545 = 0.18574 loss) | |
| I0404 13:06:04.938416 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.10877 (* 0.0454545 = 0.186762 loss) | |
| I0404 13:06:04.938429 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.60692 (* 0.0454545 = 0.163951 loss) | |
| I0404 13:06:04.938443 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.68769 (* 0.0454545 = 0.122168 loss) | |
| I0404 13:06:04.938457 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.22214 (* 0.0454545 = 0.0555518 loss) | |
| I0404 13:06:04.938470 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.22188 (* 0.0454545 = 0.0100854 loss) | |
| I0404 13:06:04.938484 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0309609 (* 0.0454545 = 0.00140731 loss) | |
| I0404 13:06:04.938498 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.012179 (* 0.0454545 = 0.000553593 loss) | |
| I0404 13:06:04.938513 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000198751 (* 0.0454545 = 9.03414e-06 loss) | |
| I0404 13:06:04.938527 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000199573 (* 0.0454545 = 9.07151e-06 loss) | |
| I0404 13:06:04.938541 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000198469 (* 0.0454545 = 9.02133e-06 loss) | |
| I0404 13:06:04.938555 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.00019866 (* 0.0454545 = 9.02998e-06 loss) | |
| I0404 13:06:04.938570 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000199126 (* 0.0454545 = 9.05118e-06 loss) | |
| I0404 13:06:04.938583 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000198756 (* 0.0454545 = 9.03438e-06 loss) | |
| I0404 13:06:04.938602 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.00019808 (* 0.0454545 = 9.00362e-06 loss) | |
| I0404 13:06:04.938640 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000199072 (* 0.0454545 = 9.04871e-06 loss) | |
| I0404 13:06:04.938657 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000198622 (* 0.0454545 = 9.02829e-06 loss) | |
| I0404 13:06:04.938670 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000197785 (* 0.0454545 = 8.99023e-06 loss) | |
| I0404 13:06:04.938684 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000197565 (* 0.0454545 = 8.98023e-06 loss) | |
| I0404 13:06:04.938699 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000198466 (* 0.0454545 = 9.02117e-06 loss) | |
| I0404 13:06:04.938710 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:06:04.938721 9252 solver.cpp:245] Train net output #45: total_confidence = 6.32013e-07 | |
| I0404 13:06:04.938736 9252 sgd_solver.cpp:106] Iteration 7000, lr = 0.00993 | |
| I0404 13:07:13.914739 9252 solver.cpp:229] Iteration 7500, loss = 1.16274 | |
| I0404 13:07:13.914877 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:07:13.914898 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:07:13.914911 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:07:13.914922 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 13:07:13.914934 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.125 | |
| I0404 13:07:13.914947 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.3125 | |
| I0404 13:07:13.914958 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.65625 | |
| I0404 13:07:13.914971 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:07:13.914983 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:07:13.914994 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:07:13.915005 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:07:13.915017 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:07:13.915029 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:07:13.915040 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:07:13.915051 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:07:13.915062 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:07:13.915074 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:07:13.915086 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:07:13.915097 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:07:13.915108 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:07:13.915120 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:07:13.915132 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:07:13.915146 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.0788 (* 0.0454545 = 0.1854 loss) | |
| I0404 13:07:13.915161 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.941 (* 0.0454545 = 0.179136 loss) | |
| I0404 13:07:13.915175 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.04383 (* 0.0454545 = 0.18381 loss) | |
| I0404 13:07:13.915189 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.17271 (* 0.0454545 = 0.189669 loss) | |
| I0404 13:07:13.915205 9252 solver.cpp:245] Train net output #26: loss/loss05 = 4.01761 (* 0.0454545 = 0.182619 loss) | |
| I0404 13:07:13.915218 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.2584 (* 0.0454545 = 0.148109 loss) | |
| I0404 13:07:13.915231 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.95521 (* 0.0454545 = 0.0888733 loss) | |
| I0404 13:07:13.915246 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.579066 (* 0.0454545 = 0.0263212 loss) | |
| I0404 13:07:13.915258 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.252006 (* 0.0454545 = 0.0114548 loss) | |
| I0404 13:07:13.915272 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0183595 (* 0.0454545 = 0.000834521 loss) | |
| I0404 13:07:13.915287 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.0004563 (* 0.0454545 = 2.07409e-05 loss) | |
| I0404 13:07:13.915302 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000454425 (* 0.0454545 = 2.06557e-05 loss) | |
| I0404 13:07:13.915316 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00045249 (* 0.0454545 = 2.05677e-05 loss) | |
| I0404 13:07:13.915330 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.0004527 (* 0.0454545 = 2.05773e-05 loss) | |
| I0404 13:07:13.915345 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000450381 (* 0.0454545 = 2.04719e-05 loss) | |
| I0404 13:07:13.915359 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000453611 (* 0.0454545 = 2.06187e-05 loss) | |
| I0404 13:07:13.915374 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000450135 (* 0.0454545 = 2.04607e-05 loss) | |
| I0404 13:07:13.915400 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.00045479 (* 0.0454545 = 2.06723e-05 loss) | |
| I0404 13:07:13.915416 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000451857 (* 0.0454545 = 2.0539e-05 loss) | |
| I0404 13:07:13.915431 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000451147 (* 0.0454545 = 2.05067e-05 loss) | |
| I0404 13:07:13.915444 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000452314 (* 0.0454545 = 2.05597e-05 loss) | |
| I0404 13:07:13.915458 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.00045076 (* 0.0454545 = 2.04891e-05 loss) | |
| I0404 13:07:13.915470 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:07:13.915482 9252 solver.cpp:245] Train net output #45: total_confidence = 2.6112e-07 | |
| I0404 13:07:13.915495 9252 sgd_solver.cpp:106] Iteration 7500, lr = 0.009925 | |
| I0404 13:08:22.993594 9252 solver.cpp:229] Iteration 8000, loss = 1.1615 | |
| I0404 13:08:22.993724 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:08:22.993747 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:08:22.993762 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 13:08:22.993773 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:08:22.993785 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 13:08:22.993798 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 13:08:22.993809 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 13:08:22.993821 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:08:22.993834 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:08:22.993845 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:08:22.993856 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:08:22.993868 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:08:22.993880 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:08:22.993891 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:08:22.993902 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:08:22.993913 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:08:22.993926 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:08:22.993937 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:08:22.993947 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:08:22.993959 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:08:22.993970 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:08:22.993981 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:08:22.993998 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.00242 (* 0.0454545 = 0.181928 loss) | |
| I0404 13:08:22.994011 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.90018 (* 0.0454545 = 0.177281 loss) | |
| I0404 13:08:22.994025 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.99023 (* 0.0454545 = 0.181374 loss) | |
| I0404 13:08:22.994040 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.02009 (* 0.0454545 = 0.182731 loss) | |
| I0404 13:08:22.994053 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.51546 (* 0.0454545 = 0.159794 loss) | |
| I0404 13:08:22.994066 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.70371 (* 0.0454545 = 0.122896 loss) | |
| I0404 13:08:22.994081 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.41929 (* 0.0454545 = 0.0645132 loss) | |
| I0404 13:08:22.994093 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.362066 (* 0.0454545 = 0.0164575 loss) | |
| I0404 13:08:22.994107 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.171919 (* 0.0454545 = 0.0078145 loss) | |
| I0404 13:08:22.994122 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.193851 (* 0.0454545 = 0.0088114 loss) | |
| I0404 13:08:22.994135 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.00106218 (* 0.0454545 = 4.82811e-05 loss) | |
| I0404 13:08:22.994149 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.00106628 (* 0.0454545 = 4.84673e-05 loss) | |
| I0404 13:08:22.994163 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00106173 (* 0.0454545 = 4.82604e-05 loss) | |
| I0404 13:08:22.994177 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.00106355 (* 0.0454545 = 4.8343e-05 loss) | |
| I0404 13:08:22.994191 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.00106203 (* 0.0454545 = 4.82739e-05 loss) | |
| I0404 13:08:22.994205 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.00105883 (* 0.0454545 = 4.81286e-05 loss) | |
| I0404 13:08:22.994220 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.0010603 (* 0.0454545 = 4.81957e-05 loss) | |
| I0404 13:08:22.994251 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.0010496 (* 0.0454545 = 4.7709e-05 loss) | |
| I0404 13:08:22.994266 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00105017 (* 0.0454545 = 4.77351e-05 loss) | |
| I0404 13:08:22.994282 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00104435 (* 0.0454545 = 4.74706e-05 loss) | |
| I0404 13:08:22.994294 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00104146 (* 0.0454545 = 4.73391e-05 loss) | |
| I0404 13:08:22.994308 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.00104541 (* 0.0454545 = 4.75186e-05 loss) | |
| I0404 13:08:22.994320 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:08:22.994331 9252 solver.cpp:245] Train net output #45: total_confidence = 6.63622e-07 | |
| I0404 13:08:22.994346 9252 sgd_solver.cpp:106] Iteration 8000, lr = 0.00992 | |
| I0404 13:09:32.232025 9252 solver.cpp:229] Iteration 8500, loss = 1.15959 | |
| I0404 13:09:32.232133 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.15625 | |
| I0404 13:09:32.232153 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:09:32.232167 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:09:32.232179 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:09:32.232192 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 13:09:32.232204 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 13:09:32.232216 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 13:09:32.232228 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:09:32.232239 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:09:32.232251 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:09:32.232264 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:09:32.232275 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:09:32.232287 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:09:32.232300 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:09:32.232311 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:09:32.232323 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:09:32.232334 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:09:32.232347 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:09:32.232358 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:09:32.232370 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:09:32.232381 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:09:32.232393 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:09:32.232409 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.8467 (* 0.0454545 = 0.17485 loss) | |
| I0404 13:09:32.232424 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.99949 (* 0.0454545 = 0.181795 loss) | |
| I0404 13:09:32.232439 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.96301 (* 0.0454545 = 0.180137 loss) | |
| I0404 13:09:32.232451 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.13149 (* 0.0454545 = 0.187795 loss) | |
| I0404 13:09:32.232465 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.69131 (* 0.0454545 = 0.167787 loss) | |
| I0404 13:09:32.232480 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.0548 (* 0.0454545 = 0.138855 loss) | |
| I0404 13:09:32.232492 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.65273 (* 0.0454545 = 0.075124 loss) | |
| I0404 13:09:32.232506 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.435187 (* 0.0454545 = 0.0197812 loss) | |
| I0404 13:09:32.232520 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.235378 (* 0.0454545 = 0.010699 loss) | |
| I0404 13:09:32.232534 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0293504 (* 0.0454545 = 0.00133411 loss) | |
| I0404 13:09:32.232549 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000747639 (* 0.0454545 = 3.39836e-05 loss) | |
| I0404 13:09:32.232563 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000749136 (* 0.0454545 = 3.40517e-05 loss) | |
| I0404 13:09:32.232578 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000744911 (* 0.0454545 = 3.38596e-05 loss) | |
| I0404 13:09:32.232591 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000743493 (* 0.0454545 = 3.37951e-05 loss) | |
| I0404 13:09:32.232605 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000744028 (* 0.0454545 = 3.38195e-05 loss) | |
| I0404 13:09:32.232620 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000748189 (* 0.0454545 = 3.40086e-05 loss) | |
| I0404 13:09:32.232633 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000745481 (* 0.0454545 = 3.38855e-05 loss) | |
| I0404 13:09:32.232664 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000737049 (* 0.0454545 = 3.35022e-05 loss) | |
| I0404 13:09:32.232679 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000736514 (* 0.0454545 = 3.34779e-05 loss) | |
| I0404 13:09:32.232693 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000731632 (* 0.0454545 = 3.3256e-05 loss) | |
| I0404 13:09:32.232707 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000730082 (* 0.0454545 = 3.31856e-05 loss) | |
| I0404 13:09:32.232722 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000734596 (* 0.0454545 = 3.33907e-05 loss) | |
| I0404 13:09:32.232733 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:09:32.232745 9252 solver.cpp:245] Train net output #45: total_confidence = 2.09044e-07 | |
| I0404 13:09:32.232759 9252 sgd_solver.cpp:106] Iteration 8500, lr = 0.009915 | |
| I0404 13:10:40.966553 9252 solver.cpp:229] Iteration 9000, loss = 1.1628 | |
| I0404 13:10:40.966686 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:10:40.966704 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:10:40.966717 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:10:40.966729 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0404 13:10:40.966742 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.09375 | |
| I0404 13:10:40.966753 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.25 | |
| I0404 13:10:40.966765 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 13:10:40.966778 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 13:10:40.966789 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:10:40.966800 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 13:10:40.966812 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:10:40.966823 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:10:40.966841 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:10:40.966855 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:10:40.966868 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:10:40.966891 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:10:40.966907 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:10:40.966919 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:10:40.966931 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:10:40.966943 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:10:40.966954 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:10:40.966965 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:10:40.966980 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.06854 (* 0.0454545 = 0.184934 loss) | |
| I0404 13:10:40.967006 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.98607 (* 0.0454545 = 0.181185 loss) | |
| I0404 13:10:40.967025 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.04329 (* 0.0454545 = 0.183786 loss) | |
| I0404 13:10:40.967038 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.16539 (* 0.0454545 = 0.189336 loss) | |
| I0404 13:10:40.967058 9252 solver.cpp:245] Train net output #26: loss/loss05 = 4.01423 (* 0.0454545 = 0.182465 loss) | |
| I0404 13:10:40.967073 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.53402 (* 0.0454545 = 0.160637 loss) | |
| I0404 13:10:40.967095 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.89142 (* 0.0454545 = 0.0859737 loss) | |
| I0404 13:10:40.967113 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.706136 (* 0.0454545 = 0.0320971 loss) | |
| I0404 13:10:40.967128 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.419656 (* 0.0454545 = 0.0190753 loss) | |
| I0404 13:10:40.967141 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.45263 (* 0.0454545 = 0.0205741 loss) | |
| I0404 13:10:40.967155 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000793393 (* 0.0454545 = 3.60633e-05 loss) | |
| I0404 13:10:40.967170 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000794267 (* 0.0454545 = 3.6103e-05 loss) | |
| I0404 13:10:40.967185 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00078933 (* 0.0454545 = 3.58786e-05 loss) | |
| I0404 13:10:40.967198 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000789809 (* 0.0454545 = 3.59004e-05 loss) | |
| I0404 13:10:40.967212 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000788265 (* 0.0454545 = 3.58302e-05 loss) | |
| I0404 13:10:40.967226 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000791121 (* 0.0454545 = 3.59601e-05 loss) | |
| I0404 13:10:40.967241 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.00078909 (* 0.0454545 = 3.58677e-05 loss) | |
| I0404 13:10:40.967272 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000783775 (* 0.0454545 = 3.56261e-05 loss) | |
| I0404 13:10:40.967288 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000782397 (* 0.0454545 = 3.55635e-05 loss) | |
| I0404 13:10:40.967303 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00078023 (* 0.0454545 = 3.5465e-05 loss) | |
| I0404 13:10:40.967316 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000775314 (* 0.0454545 = 3.52415e-05 loss) | |
| I0404 13:10:40.967330 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000780851 (* 0.0454545 = 3.54932e-05 loss) | |
| I0404 13:10:40.967342 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:10:40.967353 9252 solver.cpp:245] Train net output #45: total_confidence = 4.00002e-07 | |
| I0404 13:10:40.967366 9252 sgd_solver.cpp:106] Iteration 9000, lr = 0.00991 | |
| I0404 13:11:50.544184 9252 solver.cpp:229] Iteration 9500, loss = 1.15425 | |
| I0404 13:11:50.544386 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:11:50.544409 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:11:50.544421 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:11:50.544435 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0404 13:11:50.544446 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 13:11:50.544466 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 13:11:50.544488 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.875 | |
| I0404 13:11:50.544502 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 13:11:50.544514 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:11:50.544526 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:11:50.544538 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:11:50.544550 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:11:50.544561 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:11:50.544574 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:11:50.544585 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:11:50.544596 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:11:50.544608 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:11:50.544620 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:11:50.544631 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:11:50.544642 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:11:50.544654 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:11:50.544667 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:11:50.544682 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.11477 (* 0.0454545 = 0.187035 loss) | |
| I0404 13:11:50.544697 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.33026 (* 0.0454545 = 0.19683 loss) | |
| I0404 13:11:50.544710 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.06906 (* 0.0454545 = 0.184957 loss) | |
| I0404 13:11:50.544724 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.23636 (* 0.0454545 = 0.192562 loss) | |
| I0404 13:11:50.544739 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.59886 (* 0.0454545 = 0.163585 loss) | |
| I0404 13:11:50.544755 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.07926 (* 0.0454545 = 0.139966 loss) | |
| I0404 13:11:50.544770 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.836645 (* 0.0454545 = 0.0380293 loss) | |
| I0404 13:11:50.544785 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.213709 (* 0.0454545 = 0.00971406 loss) | |
| I0404 13:11:50.544798 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.217519 (* 0.0454545 = 0.00988724 loss) | |
| I0404 13:11:50.544813 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0138136 (* 0.0454545 = 0.00062789 loss) | |
| I0404 13:11:50.544831 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000309396 (* 0.0454545 = 1.40635e-05 loss) | |
| I0404 13:11:50.544847 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000309253 (* 0.0454545 = 1.40569e-05 loss) | |
| I0404 13:11:50.544860 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000308416 (* 0.0454545 = 1.40189e-05 loss) | |
| I0404 13:11:50.544874 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000306644 (* 0.0454545 = 1.39384e-05 loss) | |
| I0404 13:11:50.544888 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000308326 (* 0.0454545 = 1.40148e-05 loss) | |
| I0404 13:11:50.544903 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000308027 (* 0.0454545 = 1.40012e-05 loss) | |
| I0404 13:11:50.544916 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000308443 (* 0.0454545 = 1.40201e-05 loss) | |
| I0404 13:11:50.544945 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000305066 (* 0.0454545 = 1.38667e-05 loss) | |
| I0404 13:11:50.544960 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000305832 (* 0.0454545 = 1.39014e-05 loss) | |
| I0404 13:11:50.544975 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000303134 (* 0.0454545 = 1.37788e-05 loss) | |
| I0404 13:11:50.544988 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000301399 (* 0.0454545 = 1.36999e-05 loss) | |
| I0404 13:11:50.545003 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000303928 (* 0.0454545 = 1.38149e-05 loss) | |
| I0404 13:11:50.545016 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:11:50.545027 9252 solver.cpp:245] Train net output #45: total_confidence = 3.45639e-07 | |
| I0404 13:11:50.545040 9252 sgd_solver.cpp:106] Iteration 9500, lr = 0.009905 | |
| I0404 13:12:58.961078 9252 solver.cpp:338] Iteration 10000, Testing net (#0) | |
| I0404 13:13:06.986436 9252 solver.cpp:393] Test loss: 1.04821 | |
| I0404 13:13:06.986484 9252 solver.cpp:406] Test net output #0: loss/accuracy01 = 0 | |
| I0404 13:13:06.986500 9252 solver.cpp:406] Test net output #1: loss/accuracy02 = 0.117 | |
| I0404 13:13:06.986512 9252 solver.cpp:406] Test net output #2: loss/accuracy03 = 0.062 | |
| I0404 13:13:06.986524 9252 solver.cpp:406] Test net output #3: loss/accuracy04 = 0.033 | |
| I0404 13:13:06.986536 9252 solver.cpp:406] Test net output #4: loss/accuracy05 = 0.212 | |
| I0404 13:13:06.986548 9252 solver.cpp:406] Test net output #5: loss/accuracy06 = 0.501 | |
| I0404 13:13:06.986559 9252 solver.cpp:406] Test net output #6: loss/accuracy07 = 0.894 | |
| I0404 13:13:06.986570 9252 solver.cpp:406] Test net output #7: loss/accuracy08 = 0.97 | |
| I0404 13:13:06.986582 9252 solver.cpp:406] Test net output #8: loss/accuracy09 = 0.995 | |
| I0404 13:13:06.986593 9252 solver.cpp:406] Test net output #9: loss/accuracy10 = 0.998 | |
| I0404 13:13:06.986604 9252 solver.cpp:406] Test net output #10: loss/accuracy11 = 1 | |
| I0404 13:13:06.986615 9252 solver.cpp:406] Test net output #11: loss/accuracy12 = 1 | |
| I0404 13:13:06.986627 9252 solver.cpp:406] Test net output #12: loss/accuracy13 = 1 | |
| I0404 13:13:06.986639 9252 solver.cpp:406] Test net output #13: loss/accuracy14 = 1 | |
| I0404 13:13:06.986649 9252 solver.cpp:406] Test net output #14: loss/accuracy15 = 1 | |
| I0404 13:13:06.986660 9252 solver.cpp:406] Test net output #15: loss/accuracy16 = 1 | |
| I0404 13:13:06.986671 9252 solver.cpp:406] Test net output #16: loss/accuracy17 = 1 | |
| I0404 13:13:06.986682 9252 solver.cpp:406] Test net output #17: loss/accuracy18 = 1 | |
| I0404 13:13:06.986693 9252 solver.cpp:406] Test net output #18: loss/accuracy19 = 1 | |
| I0404 13:13:06.986704 9252 solver.cpp:406] Test net output #19: loss/accuracy20 = 1 | |
| I0404 13:13:06.986716 9252 solver.cpp:406] Test net output #20: loss/accuracy21 = 1 | |
| I0404 13:13:06.986727 9252 solver.cpp:406] Test net output #21: loss/accuracy22 = 1 | |
| I0404 13:13:06.986742 9252 solver.cpp:406] Test net output #22: loss/loss01 = 3.90704 (* 0.0454545 = 0.177593 loss) | |
| I0404 13:13:06.986759 9252 solver.cpp:406] Test net output #23: loss/loss02 = 3.80312 (* 0.0454545 = 0.172869 loss) | |
| I0404 13:13:06.986773 9252 solver.cpp:406] Test net output #24: loss/loss03 = 3.78051 (* 0.0454545 = 0.171841 loss) | |
| I0404 13:13:06.986786 9252 solver.cpp:406] Test net output #25: loss/loss04 = 3.9756 (* 0.0454545 = 0.180709 loss) | |
| I0404 13:13:06.986799 9252 solver.cpp:406] Test net output #26: loss/loss05 = 3.7865 (* 0.0454545 = 0.172113 loss) | |
| I0404 13:13:06.986814 9252 solver.cpp:406] Test net output #27: loss/loss06 = 2.64337 (* 0.0454545 = 0.120153 loss) | |
| I0404 13:13:06.986826 9252 solver.cpp:406] Test net output #28: loss/loss07 = 0.82762 (* 0.0454545 = 0.0376191 loss) | |
| I0404 13:13:06.986840 9252 solver.cpp:406] Test net output #29: loss/loss08 = 0.262404 (* 0.0454545 = 0.0119274 loss) | |
| I0404 13:13:06.986853 9252 solver.cpp:406] Test net output #30: loss/loss09 = 0.0509729 (* 0.0454545 = 0.00231695 loss) | |
| I0404 13:13:06.986867 9252 solver.cpp:406] Test net output #31: loss/loss10 = 0.0224028 (* 0.0454545 = 0.00101831 loss) | |
| I0404 13:13:06.986881 9252 solver.cpp:406] Test net output #32: loss/loss11 = 9.38046e-05 (* 0.0454545 = 4.26384e-06 loss) | |
| I0404 13:13:06.986896 9252 solver.cpp:406] Test net output #33: loss/loss12 = 9.45247e-05 (* 0.0454545 = 4.29658e-06 loss) | |
| I0404 13:13:06.986908 9252 solver.cpp:406] Test net output #34: loss/loss13 = 9.40204e-05 (* 0.0454545 = 4.27366e-06 loss) | |
| I0404 13:13:06.986922 9252 solver.cpp:406] Test net output #35: loss/loss14 = 9.35824e-05 (* 0.0454545 = 4.25375e-06 loss) | |
| I0404 13:13:06.986937 9252 solver.cpp:406] Test net output #36: loss/loss15 = 9.42055e-05 (* 0.0454545 = 4.28207e-06 loss) | |
| I0404 13:13:06.986950 9252 solver.cpp:406] Test net output #37: loss/loss16 = 9.37065e-05 (* 0.0454545 = 4.25939e-06 loss) | |
| I0404 13:13:06.986963 9252 solver.cpp:406] Test net output #38: loss/loss17 = 9.40283e-05 (* 0.0454545 = 4.27401e-06 loss) | |
| I0404 13:13:06.987012 9252 solver.cpp:406] Test net output #39: loss/loss18 = 9.32885e-05 (* 0.0454545 = 4.24039e-06 loss) | |
| I0404 13:13:06.987027 9252 solver.cpp:406] Test net output #40: loss/loss19 = 9.3018e-05 (* 0.0454545 = 4.22809e-06 loss) | |
| I0404 13:13:06.987041 9252 solver.cpp:406] Test net output #41: loss/loss20 = 9.22305e-05 (* 0.0454545 = 4.1923e-06 loss) | |
| I0404 13:13:06.987054 9252 solver.cpp:406] Test net output #42: loss/loss21 = 9.17886e-05 (* 0.0454545 = 4.17221e-06 loss) | |
| I0404 13:13:06.987068 9252 solver.cpp:406] Test net output #43: loss/loss22 = 9.28769e-05 (* 0.0454545 = 4.22168e-06 loss) | |
| I0404 13:13:06.987079 9252 solver.cpp:406] Test net output #44: total_accuracy = 0 | |
| I0404 13:13:06.987090 9252 solver.cpp:406] Test net output #45: total_confidence = 9.60902e-07 | |
| I0404 13:13:07.022030 9252 solver.cpp:229] Iteration 10000, loss = 1.15161 | |
| I0404 13:13:07.022068 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:13:07.022084 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:13:07.022097 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 13:13:07.022109 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 13:13:07.022122 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 13:13:07.022135 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.25 | |
| I0404 13:13:07.022146 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.65625 | |
| I0404 13:13:07.022158 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.8125 | |
| I0404 13:13:07.022171 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:13:07.022181 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 13:13:07.022193 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 0.96875 | |
| I0404 13:13:07.022205 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:13:07.022222 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:13:07.022233 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:13:07.022245 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:13:07.022256 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:13:07.022269 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:13:07.022279 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:13:07.022291 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:13:07.022303 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:13:07.022315 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:13:07.022326 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:13:07.022341 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.11306 (* 0.0454545 = 0.186957 loss) | |
| I0404 13:13:07.022354 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.02299 (* 0.0454545 = 0.182863 loss) | |
| I0404 13:13:07.022368 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.77172 (* 0.0454545 = 0.171442 loss) | |
| I0404 13:13:07.022382 9252 solver.cpp:245] Train net output #25: loss/loss04 = 4.12967 (* 0.0454545 = 0.187712 loss) | |
| I0404 13:13:07.022395 9252 solver.cpp:245] Train net output #26: loss/loss05 = 4.05748 (* 0.0454545 = 0.184431 loss) | |
| I0404 13:13:07.022409 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.58727 (* 0.0454545 = 0.163058 loss) | |
| I0404 13:13:07.022423 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.79393 (* 0.0454545 = 0.0815422 loss) | |
| I0404 13:13:07.022436 9252 solver.cpp:245] Train net output #29: loss/loss08 = 1.05243 (* 0.0454545 = 0.0478378 loss) | |
| I0404 13:13:07.022450 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.419331 (* 0.0454545 = 0.0190605 loss) | |
| I0404 13:13:07.022480 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.468521 (* 0.0454545 = 0.0212964 loss) | |
| I0404 13:13:07.022496 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.345497 (* 0.0454545 = 0.0157044 loss) | |
| I0404 13:13:07.022511 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000369944 (* 0.0454545 = 1.68156e-05 loss) | |
| I0404 13:13:07.022526 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000367904 (* 0.0454545 = 1.67229e-05 loss) | |
| I0404 13:13:07.022538 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000366626 (* 0.0454545 = 1.66648e-05 loss) | |
| I0404 13:13:07.022552 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000370039 (* 0.0454545 = 1.68199e-05 loss) | |
| I0404 13:13:07.022567 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.00036552 (* 0.0454545 = 1.66145e-05 loss) | |
| I0404 13:13:07.022580 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.00036809 (* 0.0454545 = 1.67314e-05 loss) | |
| I0404 13:13:07.022594 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000366636 (* 0.0454545 = 1.66653e-05 loss) | |
| I0404 13:13:07.022608 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000363947 (* 0.0454545 = 1.65431e-05 loss) | |
| I0404 13:13:07.022621 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000360058 (* 0.0454545 = 1.63663e-05 loss) | |
| I0404 13:13:07.022635 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00035894 (* 0.0454545 = 1.63155e-05 loss) | |
| I0404 13:13:07.022650 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000362618 (* 0.0454545 = 1.64826e-05 loss) | |
| I0404 13:13:07.022661 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:13:07.022672 9252 solver.cpp:245] Train net output #45: total_confidence = 3.57651e-07 | |
| I0404 13:13:07.022687 9252 sgd_solver.cpp:106] Iteration 10000, lr = 0.0099 | |
| I0404 13:14:16.375520 9252 solver.cpp:229] Iteration 10500, loss = 1.1474 | |
| I0404 13:14:16.375648 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:14:16.375669 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:14:16.375682 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:14:16.375695 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:14:16.375707 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 13:14:16.375720 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 13:14:16.375731 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 13:14:16.375746 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.78125 | |
| I0404 13:14:16.375758 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:14:16.375771 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:14:16.375783 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:14:16.375795 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:14:16.375807 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:14:16.375818 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:14:16.375829 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:14:16.375841 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:14:16.375852 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:14:16.375864 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:14:16.375875 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:14:16.375886 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:14:16.375898 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:14:16.375910 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:14:16.375926 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.85918 (* 0.0454545 = 0.175417 loss) | |
| I0404 13:14:16.375939 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.03737 (* 0.0454545 = 0.183517 loss) | |
| I0404 13:14:16.375953 9252 solver.cpp:245] Train net output #24: loss/loss03 = 4.03984 (* 0.0454545 = 0.183629 loss) | |
| I0404 13:14:16.375967 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.98036 (* 0.0454545 = 0.180925 loss) | |
| I0404 13:14:16.375982 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.58552 (* 0.0454545 = 0.162978 loss) | |
| I0404 13:14:16.375994 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.09797 (* 0.0454545 = 0.140817 loss) | |
| I0404 13:14:16.376008 9252 solver.cpp:245] Train net output #28: loss/loss07 = 2.10943 (* 0.0454545 = 0.0958831 loss) | |
| I0404 13:14:16.376021 9252 solver.cpp:245] Train net output #29: loss/loss08 = 1.30955 (* 0.0454545 = 0.0595251 loss) | |
| I0404 13:14:16.376035 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.392053 (* 0.0454545 = 0.0178206 loss) | |
| I0404 13:14:16.376049 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.238801 (* 0.0454545 = 0.0108546 loss) | |
| I0404 13:14:16.376063 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000334309 (* 0.0454545 = 1.51959e-05 loss) | |
| I0404 13:14:16.376078 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000333762 (* 0.0454545 = 1.5171e-05 loss) | |
| I0404 13:14:16.376092 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000329444 (* 0.0454545 = 1.49747e-05 loss) | |
| I0404 13:14:16.376106 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000327865 (* 0.0454545 = 1.49029e-05 loss) | |
| I0404 13:14:16.376121 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000330777 (* 0.0454545 = 1.50353e-05 loss) | |
| I0404 13:14:16.376134 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000327708 (* 0.0454545 = 1.48958e-05 loss) | |
| I0404 13:14:16.376148 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000331224 (* 0.0454545 = 1.50556e-05 loss) | |
| I0404 13:14:16.376179 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000331396 (* 0.0454545 = 1.50635e-05 loss) | |
| I0404 13:14:16.376194 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000327792 (* 0.0454545 = 1.48996e-05 loss) | |
| I0404 13:14:16.376209 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000323351 (* 0.0454545 = 1.46978e-05 loss) | |
| I0404 13:14:16.376221 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000320853 (* 0.0454545 = 1.45842e-05 loss) | |
| I0404 13:14:16.376235 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000324341 (* 0.0454545 = 1.47428e-05 loss) | |
| I0404 13:14:16.376247 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:14:16.376260 9252 solver.cpp:245] Train net output #45: total_confidence = 3.59432e-07 | |
| I0404 13:14:16.376273 9252 sgd_solver.cpp:106] Iteration 10500, lr = 0.009895 | |
| I0404 13:15:25.642549 9252 solver.cpp:229] Iteration 11000, loss = 1.13871 | |
| I0404 13:15:25.642679 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0 | |
| I0404 13:15:25.642699 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:15:25.642719 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:15:25.642740 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 13:15:25.642752 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 13:15:25.642765 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 13:15:25.642777 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:15:25.642789 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:15:25.642802 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:15:25.642812 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:15:25.642824 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:15:25.642837 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:15:25.642848 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:15:25.642859 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:15:25.642870 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:15:25.642882 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:15:25.642894 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:15:25.642909 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:15:25.642920 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:15:25.642932 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:15:25.642945 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:15:25.642956 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:15:25.642971 9252 solver.cpp:245] Train net output #22: loss/loss01 = 4.09146 (* 0.0454545 = 0.185975 loss) | |
| I0404 13:15:25.642985 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.84972 (* 0.0454545 = 0.174987 loss) | |
| I0404 13:15:25.642999 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.65608 (* 0.0454545 = 0.166186 loss) | |
| I0404 13:15:25.643013 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.69609 (* 0.0454545 = 0.168004 loss) | |
| I0404 13:15:25.643026 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.28622 (* 0.0454545 = 0.149374 loss) | |
| I0404 13:15:25.643040 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.49898 (* 0.0454545 = 0.11359 loss) | |
| I0404 13:15:25.643054 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.53784 (* 0.0454545 = 0.0699018 loss) | |
| I0404 13:15:25.643067 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.603318 (* 0.0454545 = 0.0274235 loss) | |
| I0404 13:15:25.643081 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.387639 (* 0.0454545 = 0.01762 loss) | |
| I0404 13:15:25.643096 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.217075 (* 0.0454545 = 0.00986703 loss) | |
| I0404 13:15:25.643110 9252 solver.cpp:245] Train net output #32: loss/loss11 = 7.99725e-05 (* 0.0454545 = 3.63511e-06 loss) | |
| I0404 13:15:25.643124 9252 solver.cpp:245] Train net output #33: loss/loss12 = 8.02258e-05 (* 0.0454545 = 3.64663e-06 loss) | |
| I0404 13:15:25.643138 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.67825e-05 (* 0.0454545 = 3.49011e-06 loss) | |
| I0404 13:15:25.643152 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.77551e-05 (* 0.0454545 = 3.53432e-06 loss) | |
| I0404 13:15:25.643167 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.86811e-05 (* 0.0454545 = 3.57641e-06 loss) | |
| I0404 13:15:25.643180 9252 solver.cpp:245] Train net output #37: loss/loss16 = 7.4271e-05 (* 0.0454545 = 3.37595e-06 loss) | |
| I0404 13:15:25.643194 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.73155e-05 (* 0.0454545 = 3.51434e-06 loss) | |
| I0404 13:15:25.643234 9252 solver.cpp:245] Train net output #39: loss/loss18 = 8.06077e-05 (* 0.0454545 = 3.66399e-06 loss) | |
| I0404 13:15:25.643267 9252 solver.cpp:245] Train net output #40: loss/loss19 = 7.39466e-05 (* 0.0454545 = 3.36121e-06 loss) | |
| I0404 13:15:25.643298 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.15393e-05 (* 0.0454545 = 3.25179e-06 loss) | |
| I0404 13:15:25.643322 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.34174e-05 (* 0.0454545 = 3.33716e-06 loss) | |
| I0404 13:15:25.643347 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.29144e-05 (* 0.0454545 = 3.31429e-06 loss) | |
| I0404 13:15:25.643362 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:15:25.643374 9252 solver.cpp:245] Train net output #45: total_confidence = 3.65341e-06 | |
| I0404 13:15:25.643388 9252 sgd_solver.cpp:106] Iteration 11000, lr = 0.00989 | |
| I0404 13:16:34.706630 9252 solver.cpp:229] Iteration 11500, loss = 1.12827 | |
| I0404 13:16:34.706781 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:16:34.706802 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:16:34.706815 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:16:34.706827 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:16:34.706840 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 13:16:34.706851 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 13:16:34.706863 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 13:16:34.706874 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:16:34.706887 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:16:34.706898 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:16:34.706909 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:16:34.706921 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:16:34.706933 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:16:34.706943 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:16:34.706955 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:16:34.706967 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:16:34.706979 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:16:34.706990 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:16:34.707002 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:16:34.707013 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:16:34.707026 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:16:34.707036 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:16:34.707052 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.99769 (* 0.0454545 = 0.181713 loss) | |
| I0404 13:16:34.707067 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.9706 (* 0.0454545 = 0.180482 loss) | |
| I0404 13:16:34.707080 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.77047 (* 0.0454545 = 0.171385 loss) | |
| I0404 13:16:34.707094 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.74601 (* 0.0454545 = 0.170273 loss) | |
| I0404 13:16:34.707108 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.36795 (* 0.0454545 = 0.153089 loss) | |
| I0404 13:16:34.707121 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.70214 (* 0.0454545 = 0.122825 loss) | |
| I0404 13:16:34.707135 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.05943 (* 0.0454545 = 0.0481559 loss) | |
| I0404 13:16:34.707149 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.402296 (* 0.0454545 = 0.0182862 loss) | |
| I0404 13:16:34.707164 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.19762 (* 0.0454545 = 0.00898271 loss) | |
| I0404 13:16:34.707177 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0206675 (* 0.0454545 = 0.000939434 loss) | |
| I0404 13:16:34.707191 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000229966 (* 0.0454545 = 1.0453e-05 loss) | |
| I0404 13:16:34.707206 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000229787 (* 0.0454545 = 1.04449e-05 loss) | |
| I0404 13:16:34.707219 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000214832 (* 0.0454545 = 9.76509e-06 loss) | |
| I0404 13:16:34.707233 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000216168 (* 0.0454545 = 9.82581e-06 loss) | |
| I0404 13:16:34.707247 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.00020873 (* 0.0454545 = 9.48772e-06 loss) | |
| I0404 13:16:34.707262 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000223521 (* 0.0454545 = 1.016e-05 loss) | |
| I0404 13:16:34.707275 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.00021709 (* 0.0454545 = 9.86774e-06 loss) | |
| I0404 13:16:34.707307 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000206786 (* 0.0454545 = 9.39936e-06 loss) | |
| I0404 13:16:34.707322 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000204504 (* 0.0454545 = 9.29565e-06 loss) | |
| I0404 13:16:34.707336 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000206727 (* 0.0454545 = 9.39667e-06 loss) | |
| I0404 13:16:34.707350 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000203244 (* 0.0454545 = 9.23838e-06 loss) | |
| I0404 13:16:34.707365 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000211578 (* 0.0454545 = 9.61718e-06 loss) | |
| I0404 13:16:34.707376 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:16:34.707388 9252 solver.cpp:245] Train net output #45: total_confidence = 2.05411e-06 | |
| I0404 13:16:34.707402 9252 sgd_solver.cpp:106] Iteration 11500, lr = 0.009885 | |
| I0404 13:17:43.138348 9252 solver.cpp:229] Iteration 12000, loss = 1.1232 | |
| I0404 13:17:43.138459 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:17:43.138479 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:17:43.138492 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:17:43.138504 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:17:43.138519 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 13:17:43.138530 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.3125 | |
| I0404 13:17:43.138542 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 13:17:43.138555 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:17:43.138566 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:17:43.138577 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:17:43.138589 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:17:43.138602 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:17:43.138619 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:17:43.138630 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:17:43.138643 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:17:43.138653 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:17:43.138665 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:17:43.138681 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:17:43.138692 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:17:43.138703 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:17:43.138715 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:17:43.138726 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:17:43.138742 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.94731 (* 0.0454545 = 0.179423 loss) | |
| I0404 13:17:43.138761 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.74095 (* 0.0454545 = 0.170043 loss) | |
| I0404 13:17:43.138774 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.58563 (* 0.0454545 = 0.162983 loss) | |
| I0404 13:17:43.138788 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.74082 (* 0.0454545 = 0.170037 loss) | |
| I0404 13:17:43.138802 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.23789 (* 0.0454545 = 0.147177 loss) | |
| I0404 13:17:43.138815 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.06757 (* 0.0454545 = 0.139435 loss) | |
| I0404 13:17:43.138828 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.84067 (* 0.0454545 = 0.0836666 loss) | |
| I0404 13:17:43.138842 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.465982 (* 0.0454545 = 0.021181 loss) | |
| I0404 13:17:43.138855 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.391401 (* 0.0454545 = 0.017791 loss) | |
| I0404 13:17:43.138870 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.010997 (* 0.0454545 = 0.000499862 loss) | |
| I0404 13:17:43.138893 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.47288e-05 (* 0.0454545 = 2.94222e-06 loss) | |
| I0404 13:17:43.138907 9252 solver.cpp:245] Train net output #33: loss/loss12 = 6.24744e-05 (* 0.0454545 = 2.83974e-06 loss) | |
| I0404 13:17:43.138921 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.97447e-05 (* 0.0454545 = 2.71567e-06 loss) | |
| I0404 13:17:43.138936 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.0732e-05 (* 0.0454545 = 2.76054e-06 loss) | |
| I0404 13:17:43.138950 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.79386e-05 (* 0.0454545 = 2.63357e-06 loss) | |
| I0404 13:17:43.138963 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.09696e-05 (* 0.0454545 = 2.77134e-06 loss) | |
| I0404 13:17:43.138978 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.99162e-05 (* 0.0454545 = 2.72346e-06 loss) | |
| I0404 13:17:43.139008 9252 solver.cpp:245] Train net output #39: loss/loss18 = 6.08167e-05 (* 0.0454545 = 2.7644e-06 loss) | |
| I0404 13:17:43.139029 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.72049e-05 (* 0.0454545 = 2.60022e-06 loss) | |
| I0404 13:17:43.139042 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.6799e-05 (* 0.0454545 = 2.58177e-06 loss) | |
| I0404 13:17:43.139056 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.60273e-05 (* 0.0454545 = 2.5467e-06 loss) | |
| I0404 13:17:43.139070 9252 solver.cpp:245] Train net output #43: loss/loss22 = 5.89625e-05 (* 0.0454545 = 2.68012e-06 loss) | |
| I0404 13:17:43.139088 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:17:43.139099 9252 solver.cpp:245] Train net output #45: total_confidence = 2.28848e-06 | |
| I0404 13:17:43.139113 9252 sgd_solver.cpp:106] Iteration 12000, lr = 0.00988 | |
| I0404 13:18:52.937170 9252 solver.cpp:229] Iteration 12500, loss = 1.12429 | |
| I0404 13:18:52.937296 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:18:52.937315 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 13:18:52.937331 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 13:18:52.937345 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 13:18:52.937356 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 13:18:52.937368 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 13:18:52.937381 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 13:18:52.937392 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:18:52.937405 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:18:52.937428 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:18:52.937444 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:18:52.937456 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:18:52.937469 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:18:52.937480 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:18:52.937491 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:18:52.937504 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:18:52.937515 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:18:52.937526 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:18:52.937538 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:18:52.937549 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:18:52.937561 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:18:52.937572 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:18:52.937588 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.93503 (* 0.0454545 = 0.178865 loss) | |
| I0404 13:18:52.937603 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.82563 (* 0.0454545 = 0.173892 loss) | |
| I0404 13:18:52.937618 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.68144 (* 0.0454545 = 0.167338 loss) | |
| I0404 13:18:52.937630 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.84803 (* 0.0454545 = 0.174911 loss) | |
| I0404 13:18:52.937644 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.5899 (* 0.0454545 = 0.163177 loss) | |
| I0404 13:18:52.937659 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.82444 (* 0.0454545 = 0.128384 loss) | |
| I0404 13:18:52.937671 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.37427 (* 0.0454545 = 0.0624669 loss) | |
| I0404 13:18:52.937685 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.490022 (* 0.0454545 = 0.0222737 loss) | |
| I0404 13:18:52.937698 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.277087 (* 0.0454545 = 0.0125949 loss) | |
| I0404 13:18:52.937713 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0409232 (* 0.0454545 = 0.00186014 loss) | |
| I0404 13:18:52.937727 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000591626 (* 0.0454545 = 2.68921e-05 loss) | |
| I0404 13:18:52.937741 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000576264 (* 0.0454545 = 2.61938e-05 loss) | |
| I0404 13:18:52.937755 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000546729 (* 0.0454545 = 2.48513e-05 loss) | |
| I0404 13:18:52.937769 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000560714 (* 0.0454545 = 2.5487e-05 loss) | |
| I0404 13:18:52.937784 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000525979 (* 0.0454545 = 2.39081e-05 loss) | |
| I0404 13:18:52.937798 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000564712 (* 0.0454545 = 2.56687e-05 loss) | |
| I0404 13:18:52.937813 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000554413 (* 0.0454545 = 2.52006e-05 loss) | |
| I0404 13:18:52.937844 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000547902 (* 0.0454545 = 2.49047e-05 loss) | |
| I0404 13:18:52.937860 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000533652 (* 0.0454545 = 2.42569e-05 loss) | |
| I0404 13:18:52.937875 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000533421 (* 0.0454545 = 2.42464e-05 loss) | |
| I0404 13:18:52.937888 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000526325 (* 0.0454545 = 2.39239e-05 loss) | |
| I0404 13:18:52.937902 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000553403 (* 0.0454545 = 2.51547e-05 loss) | |
| I0404 13:18:52.937914 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:18:52.937927 9252 solver.cpp:245] Train net output #45: total_confidence = 1.08969e-06 | |
| I0404 13:18:52.937942 9252 sgd_solver.cpp:106] Iteration 12500, lr = 0.009875 | |
| I0404 13:20:01.905992 9252 solver.cpp:229] Iteration 13000, loss = 1.1115 | |
| I0404 13:20:01.906157 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 13:20:01.906179 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:20:01.906193 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:20:01.906204 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:20:01.906216 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 13:20:01.906229 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 13:20:01.906241 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 13:20:01.906252 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:20:01.906265 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:20:01.906277 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:20:01.906288 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:20:01.906301 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:20:01.906311 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:20:01.906323 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:20:01.906334 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:20:01.906347 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:20:01.906364 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:20:01.906378 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:20:01.906389 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:20:01.906400 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:20:01.906412 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:20:01.906424 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:20:01.906440 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.78647 (* 0.0454545 = 0.172112 loss) | |
| I0404 13:20:01.906455 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.96421 (* 0.0454545 = 0.180191 loss) | |
| I0404 13:20:01.906468 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.66072 (* 0.0454545 = 0.166396 loss) | |
| I0404 13:20:01.906481 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.81004 (* 0.0454545 = 0.173183 loss) | |
| I0404 13:20:01.906496 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.51474 (* 0.0454545 = 0.159761 loss) | |
| I0404 13:20:01.906509 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.77237 (* 0.0454545 = 0.126017 loss) | |
| I0404 13:20:01.906522 9252 solver.cpp:245] Train net output #28: loss/loss07 = 2.07401 (* 0.0454545 = 0.0942731 loss) | |
| I0404 13:20:01.906536 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.515432 (* 0.0454545 = 0.0234287 loss) | |
| I0404 13:20:01.906550 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.103569 (* 0.0454545 = 0.0047077 loss) | |
| I0404 13:20:01.906564 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0350567 (* 0.0454545 = 0.00159349 loss) | |
| I0404 13:20:01.906579 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000344659 (* 0.0454545 = 1.56663e-05 loss) | |
| I0404 13:20:01.906594 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000328942 (* 0.0454545 = 1.49519e-05 loss) | |
| I0404 13:20:01.906607 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000316555 (* 0.0454545 = 1.43889e-05 loss) | |
| I0404 13:20:01.906620 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000327042 (* 0.0454545 = 1.48655e-05 loss) | |
| I0404 13:20:01.906635 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000306534 (* 0.0454545 = 1.39334e-05 loss) | |
| I0404 13:20:01.906649 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000316207 (* 0.0454545 = 1.4373e-05 loss) | |
| I0404 13:20:01.906664 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000314842 (* 0.0454545 = 1.4311e-05 loss) | |
| I0404 13:20:01.906695 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000327846 (* 0.0454545 = 1.49021e-05 loss) | |
| I0404 13:20:01.906720 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00030785 (* 0.0454545 = 1.39932e-05 loss) | |
| I0404 13:20:01.906735 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000304301 (* 0.0454545 = 1.38319e-05 loss) | |
| I0404 13:20:01.906752 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000304177 (* 0.0454545 = 1.38262e-05 loss) | |
| I0404 13:20:01.906767 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000315185 (* 0.0454545 = 1.43266e-05 loss) | |
| I0404 13:20:01.906780 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:20:01.906791 9252 solver.cpp:245] Train net output #45: total_confidence = 2.73727e-06 | |
| I0404 13:20:01.906805 9252 sgd_solver.cpp:106] Iteration 13000, lr = 0.00987 | |
| I0404 13:21:11.995949 9252 solver.cpp:229] Iteration 13500, loss = 1.10649 | |
| I0404 13:21:11.996109 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:21:11.996129 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:21:11.996141 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 13:21:11.996155 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:21:11.996166 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 13:21:11.996178 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 13:21:11.996191 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:21:11.996202 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 13:21:11.996214 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.875 | |
| I0404 13:21:11.996225 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 13:21:11.996237 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:21:11.996248 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:21:11.996260 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:21:11.996271 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:21:11.996284 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:21:11.996294 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:21:11.996306 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:21:11.996317 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:21:11.996328 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:21:11.996340 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:21:11.996351 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:21:11.996363 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:21:11.996378 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.96436 (* 0.0454545 = 0.180198 loss) | |
| I0404 13:21:11.996392 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.62556 (* 0.0454545 = 0.164798 loss) | |
| I0404 13:21:11.996407 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.76237 (* 0.0454545 = 0.171017 loss) | |
| I0404 13:21:11.996420 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.86595 (* 0.0454545 = 0.175725 loss) | |
| I0404 13:21:11.996434 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.54523 (* 0.0454545 = 0.161147 loss) | |
| I0404 13:21:11.996448 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.93352 (* 0.0454545 = 0.133342 loss) | |
| I0404 13:21:11.996461 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.64461 (* 0.0454545 = 0.0747551 loss) | |
| I0404 13:21:11.996475 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.987047 (* 0.0454545 = 0.0448658 loss) | |
| I0404 13:21:11.996490 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.80871 (* 0.0454545 = 0.0367596 loss) | |
| I0404 13:21:11.996503 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.470666 (* 0.0454545 = 0.0213939 loss) | |
| I0404 13:21:11.996517 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.73052e-05 (* 0.0454545 = 1.69569e-06 loss) | |
| I0404 13:21:11.996531 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.43206e-05 (* 0.0454545 = 1.56003e-06 loss) | |
| I0404 13:21:11.996546 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.34935e-05 (* 0.0454545 = 1.52243e-06 loss) | |
| I0404 13:21:11.996561 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.48087e-05 (* 0.0454545 = 1.58222e-06 loss) | |
| I0404 13:21:11.996574 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.3106e-05 (* 0.0454545 = 1.50482e-06 loss) | |
| I0404 13:21:11.996588 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.26122e-05 (* 0.0454545 = 1.48237e-06 loss) | |
| I0404 13:21:11.996601 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.26681e-05 (* 0.0454545 = 1.48491e-06 loss) | |
| I0404 13:21:11.996633 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.80655e-05 (* 0.0454545 = 1.73025e-06 loss) | |
| I0404 13:21:11.996647 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.31898e-05 (* 0.0454545 = 1.50863e-06 loss) | |
| I0404 13:21:11.996661 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.14758e-05 (* 0.0454545 = 1.43072e-06 loss) | |
| I0404 13:21:11.996675 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.28843e-05 (* 0.0454545 = 1.49474e-06 loss) | |
| I0404 13:21:11.996690 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.25004e-05 (* 0.0454545 = 1.47729e-06 loss) | |
| I0404 13:21:11.996701 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:21:11.996713 9252 solver.cpp:245] Train net output #45: total_confidence = 3.36076e-06 | |
| I0404 13:21:11.996728 9252 sgd_solver.cpp:106] Iteration 13500, lr = 0.009865 | |
| I0404 13:22:21.233260 9252 solver.cpp:229] Iteration 14000, loss = 1.10528 | |
| I0404 13:22:21.233400 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:22:21.233433 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:22:21.233458 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:22:21.233481 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:22:21.233505 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 13:22:21.233549 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 13:22:21.233578 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:22:21.233600 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:22:21.233623 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:22:21.233654 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:22:21.233678 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:22:21.233700 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:22:21.233721 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:22:21.233747 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:22:21.233770 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:22:21.233790 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:22:21.233813 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:22:21.233834 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:22:21.233855 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:22:21.233876 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:22:21.233898 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:22:21.233922 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:22:21.233953 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.69069 (* 0.0454545 = 0.167759 loss) | |
| I0404 13:22:21.233991 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.79573 (* 0.0454545 = 0.172533 loss) | |
| I0404 13:22:21.234019 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.81568 (* 0.0454545 = 0.17344 loss) | |
| I0404 13:22:21.234045 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.76808 (* 0.0454545 = 0.171276 loss) | |
| I0404 13:22:21.234072 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.64036 (* 0.0454545 = 0.165471 loss) | |
| I0404 13:22:21.234099 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.97326 (* 0.0454545 = 0.135148 loss) | |
| I0404 13:22:21.234125 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.79657 (* 0.0454545 = 0.0816621 loss) | |
| I0404 13:22:21.234151 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.426793 (* 0.0454545 = 0.0193997 loss) | |
| I0404 13:22:21.234177 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.229527 (* 0.0454545 = 0.0104331 loss) | |
| I0404 13:22:21.234205 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0127983 (* 0.0454545 = 0.000581741 loss) | |
| I0404 13:22:21.234231 9252 solver.cpp:245] Train net output #32: loss/loss11 = 7.28919e-05 (* 0.0454545 = 3.31327e-06 loss) | |
| I0404 13:22:21.234258 9252 solver.cpp:245] Train net output #33: loss/loss12 = 6.91375e-05 (* 0.0454545 = 3.14262e-06 loss) | |
| I0404 13:22:21.234283 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.75534e-05 (* 0.0454545 = 3.07061e-06 loss) | |
| I0404 13:22:21.234311 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.90701e-05 (* 0.0454545 = 3.13955e-06 loss) | |
| I0404 13:22:21.234338 9252 solver.cpp:245] Train net output #36: loss/loss15 = 6.32675e-05 (* 0.0454545 = 2.8758e-06 loss) | |
| I0404 13:22:21.234364 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.82361e-05 (* 0.0454545 = 3.10164e-06 loss) | |
| I0404 13:22:21.234391 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.6182e-05 (* 0.0454545 = 3.00827e-06 loss) | |
| I0404 13:22:21.234447 9252 solver.cpp:245] Train net output #39: loss/loss18 = 6.74257e-05 (* 0.0454545 = 3.06481e-06 loss) | |
| I0404 13:22:21.234477 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.63162e-05 (* 0.0454545 = 3.01437e-06 loss) | |
| I0404 13:22:21.234503 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.49413e-05 (* 0.0454545 = 2.95188e-06 loss) | |
| I0404 13:22:21.234529 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.39441e-05 (* 0.0454545 = 2.90655e-06 loss) | |
| I0404 13:22:21.234556 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.88247e-05 (* 0.0454545 = 3.1284e-06 loss) | |
| I0404 13:22:21.234580 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:22:21.234601 9252 solver.cpp:245] Train net output #45: total_confidence = 1.03075e-05 | |
| I0404 13:22:21.234625 9252 sgd_solver.cpp:106] Iteration 14000, lr = 0.00986 | |
| I0404 13:23:30.643450 9252 solver.cpp:229] Iteration 14500, loss = 1.10073 | |
| I0404 13:23:30.643568 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:23:30.643599 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:23:30.643623 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 13:23:30.643648 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:23:30.643671 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 13:23:30.643695 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 13:23:30.643721 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.875 | |
| I0404 13:23:30.643745 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:23:30.643769 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:23:30.643801 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:23:30.643823 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:23:30.643846 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:23:30.643867 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:23:30.643889 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:23:30.643915 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:23:30.643937 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:23:30.643959 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:23:30.643981 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:23:30.644002 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:23:30.644023 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:23:30.644045 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:23:30.644068 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:23:30.644101 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.94932 (* 0.0454545 = 0.179515 loss) | |
| I0404 13:23:30.644140 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.85699 (* 0.0454545 = 0.175318 loss) | |
| I0404 13:23:30.644167 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.84809 (* 0.0454545 = 0.174913 loss) | |
| I0404 13:23:30.644193 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.9926 (* 0.0454545 = 0.181482 loss) | |
| I0404 13:23:30.644220 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.70649 (* 0.0454545 = 0.168477 loss) | |
| I0404 13:23:30.644246 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.60656 (* 0.0454545 = 0.11848 loss) | |
| I0404 13:23:30.644273 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.878777 (* 0.0454545 = 0.0399444 loss) | |
| I0404 13:23:30.644299 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.422505 (* 0.0454545 = 0.0192048 loss) | |
| I0404 13:23:30.644326 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.154408 (* 0.0454545 = 0.00701852 loss) | |
| I0404 13:23:30.644353 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.185971 (* 0.0454545 = 0.00845322 loss) | |
| I0404 13:23:30.644381 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000106894 (* 0.0454545 = 4.85882e-06 loss) | |
| I0404 13:23:30.644409 9252 solver.cpp:245] Train net output #33: loss/loss12 = 9.91565e-05 (* 0.0454545 = 4.50712e-06 loss) | |
| I0404 13:23:30.644434 9252 solver.cpp:245] Train net output #34: loss/loss13 = 9.82944e-05 (* 0.0454545 = 4.46793e-06 loss) | |
| I0404 13:23:30.644461 9252 solver.cpp:245] Train net output #35: loss/loss14 = 9.98585e-05 (* 0.0454545 = 4.53902e-06 loss) | |
| I0404 13:23:30.644489 9252 solver.cpp:245] Train net output #36: loss/loss15 = 9.13147e-05 (* 0.0454545 = 4.15067e-06 loss) | |
| I0404 13:23:30.644515 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000102023 (* 0.0454545 = 4.6374e-06 loss) | |
| I0404 13:23:30.644546 9252 solver.cpp:245] Train net output #38: loss/loss17 = 9.67701e-05 (* 0.0454545 = 4.39864e-06 loss) | |
| I0404 13:23:30.644594 9252 solver.cpp:245] Train net output #39: loss/loss18 = 9.53894e-05 (* 0.0454545 = 4.33588e-06 loss) | |
| I0404 13:23:30.644624 9252 solver.cpp:245] Train net output #40: loss/loss19 = 9.68782e-05 (* 0.0454545 = 4.40355e-06 loss) | |
| I0404 13:23:30.644651 9252 solver.cpp:245] Train net output #41: loss/loss20 = 9.54994e-05 (* 0.0454545 = 4.34088e-06 loss) | |
| I0404 13:23:30.644677 9252 solver.cpp:245] Train net output #42: loss/loss21 = 9.29173e-05 (* 0.0454545 = 4.22351e-06 loss) | |
| I0404 13:23:30.644704 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000100681 (* 0.0454545 = 4.5764e-06 loss) | |
| I0404 13:23:30.644726 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:23:30.644748 9252 solver.cpp:245] Train net output #45: total_confidence = 6.44242e-06 | |
| I0404 13:23:30.644772 9252 sgd_solver.cpp:106] Iteration 14500, lr = 0.009855 | |
| I0404 13:24:40.195698 9252 solver.cpp:229] Iteration 15000, loss = 1.09614 | |
| I0404 13:24:40.195850 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 13:24:40.195881 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 13:24:40.195906 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 13:24:40.195929 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 13:24:40.195955 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 13:24:40.195978 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 13:24:40.196004 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.59375 | |
| I0404 13:24:40.196027 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.78125 | |
| I0404 13:24:40.196048 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:24:40.196070 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:24:40.196091 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:24:40.196113 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:24:40.196135 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:24:40.196156 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:24:40.196177 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:24:40.196198 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:24:40.196219 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:24:40.196240 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:24:40.196262 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:24:40.196283 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:24:40.196305 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:24:40.196326 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:24:40.196353 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.77107 (* 0.0454545 = 0.171412 loss) | |
| I0404 13:24:40.196384 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.00233 (* 0.0454545 = 0.181924 loss) | |
| I0404 13:24:40.196415 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.87574 (* 0.0454545 = 0.17617 loss) | |
| I0404 13:24:40.196442 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.80348 (* 0.0454545 = 0.172886 loss) | |
| I0404 13:24:40.196470 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.97467 (* 0.0454545 = 0.180667 loss) | |
| I0404 13:24:40.196496 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.95732 (* 0.0454545 = 0.134424 loss) | |
| I0404 13:24:40.196534 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.97666 (* 0.0454545 = 0.089848 loss) | |
| I0404 13:24:40.196562 9252 solver.cpp:245] Train net output #29: loss/loss08 = 1.55165 (* 0.0454545 = 0.0705295 loss) | |
| I0404 13:24:40.196588 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0456646 (* 0.0454545 = 0.00207566 loss) | |
| I0404 13:24:40.196614 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0180803 (* 0.0454545 = 0.00082183 loss) | |
| I0404 13:24:40.196641 9252 solver.cpp:245] Train net output #32: loss/loss11 = 9.92597e-05 (* 0.0454545 = 4.5118e-06 loss) | |
| I0404 13:24:40.196668 9252 solver.cpp:245] Train net output #33: loss/loss12 = 9.10176e-05 (* 0.0454545 = 4.13716e-06 loss) | |
| I0404 13:24:40.196694 9252 solver.cpp:245] Train net output #34: loss/loss13 = 9.43176e-05 (* 0.0454545 = 4.28716e-06 loss) | |
| I0404 13:24:40.196720 9252 solver.cpp:245] Train net output #35: loss/loss14 = 9.47759e-05 (* 0.0454545 = 4.308e-06 loss) | |
| I0404 13:24:40.196748 9252 solver.cpp:245] Train net output #36: loss/loss15 = 9.00114e-05 (* 0.0454545 = 4.09143e-06 loss) | |
| I0404 13:24:40.196775 9252 solver.cpp:245] Train net output #37: loss/loss16 = 8.97143e-05 (* 0.0454545 = 4.07792e-06 loss) | |
| I0404 13:24:40.196801 9252 solver.cpp:245] Train net output #38: loss/loss17 = 8.83315e-05 (* 0.0454545 = 4.01507e-06 loss) | |
| I0404 13:24:40.196846 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000102463 (* 0.0454545 = 4.65742e-06 loss) | |
| I0404 13:24:40.196878 9252 solver.cpp:245] Train net output #40: loss/loss19 = 9.26795e-05 (* 0.0454545 = 4.2127e-06 loss) | |
| I0404 13:24:40.196908 9252 solver.cpp:245] Train net output #41: loss/loss20 = 8.73775e-05 (* 0.0454545 = 3.97171e-06 loss) | |
| I0404 13:24:40.196935 9252 solver.cpp:245] Train net output #42: loss/loss21 = 9.06409e-05 (* 0.0454545 = 4.12004e-06 loss) | |
| I0404 13:24:40.196962 9252 solver.cpp:245] Train net output #43: loss/loss22 = 9.02209e-05 (* 0.0454545 = 4.10095e-06 loss) | |
| I0404 13:24:40.196985 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:24:40.197010 9252 solver.cpp:245] Train net output #45: total_confidence = 1.18823e-05 | |
| I0404 13:24:40.197036 9252 sgd_solver.cpp:106] Iteration 15000, lr = 0.00985 | |
| I0404 13:25:49.816247 9252 solver.cpp:229] Iteration 15500, loss = 1.09317 | |
| I0404 13:25:49.816355 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:25:49.816386 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 13:25:49.816411 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:25:49.816434 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:25:49.816457 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 13:25:49.816480 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 13:25:49.816504 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 13:25:49.816529 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 1 | |
| I0404 13:25:49.816551 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:25:49.816573 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:25:49.816596 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:25:49.816618 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:25:49.816639 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:25:49.816660 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:25:49.816682 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:25:49.816704 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:25:49.816725 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:25:49.816747 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:25:49.816769 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:25:49.816792 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:25:49.816813 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:25:49.816834 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:25:49.816862 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.64095 (* 0.0454545 = 0.165498 loss) | |
| I0404 13:25:49.816890 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.85141 (* 0.0454545 = 0.175064 loss) | |
| I0404 13:25:49.816920 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.86111 (* 0.0454545 = 0.175505 loss) | |
| I0404 13:25:49.816949 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.82123 (* 0.0454545 = 0.173692 loss) | |
| I0404 13:25:49.816977 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.44434 (* 0.0454545 = 0.156561 loss) | |
| I0404 13:25:49.817006 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.68898 (* 0.0454545 = 0.122226 loss) | |
| I0404 13:25:49.817034 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.11653 (* 0.0454545 = 0.0507513 loss) | |
| I0404 13:25:49.817071 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.0457279 (* 0.0454545 = 0.00207854 loss) | |
| I0404 13:25:49.817100 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0110422 (* 0.0454545 = 0.000501916 loss) | |
| I0404 13:25:49.817126 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00378135 (* 0.0454545 = 0.000171879 loss) | |
| I0404 13:25:49.817154 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.71764e-05 (* 0.0454545 = 1.68984e-06 loss) | |
| I0404 13:25:49.817183 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.53507e-05 (* 0.0454545 = 1.60685e-06 loss) | |
| I0404 13:25:49.817209 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.52501e-05 (* 0.0454545 = 1.60228e-06 loss) | |
| I0404 13:25:49.817236 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.66735e-05 (* 0.0454545 = 1.66698e-06 loss) | |
| I0404 13:25:49.817262 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.25376e-05 (* 0.0454545 = 1.47898e-06 loss) | |
| I0404 13:25:49.817291 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.42068e-05 (* 0.0454545 = 1.55486e-06 loss) | |
| I0404 13:25:49.817317 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.45497e-05 (* 0.0454545 = 1.57044e-06 loss) | |
| I0404 13:25:49.817366 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.44937e-05 (* 0.0454545 = 1.5679e-06 loss) | |
| I0404 13:25:49.817416 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.45794e-05 (* 0.0454545 = 1.57179e-06 loss) | |
| I0404 13:25:49.817447 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.40243e-05 (* 0.0454545 = 1.54656e-06 loss) | |
| I0404 13:25:49.817474 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.27649e-05 (* 0.0454545 = 1.48931e-06 loss) | |
| I0404 13:25:49.817500 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.46093e-05 (* 0.0454545 = 1.57315e-06 loss) | |
| I0404 13:25:49.817523 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:25:49.817544 9252 solver.cpp:245] Train net output #45: total_confidence = 5.6622e-05 | |
| I0404 13:25:49.817566 9252 sgd_solver.cpp:106] Iteration 15500, lr = 0.009845 | |
| I0404 13:26:58.920061 9252 solver.cpp:229] Iteration 16000, loss = 1.08803 | |
| I0404 13:26:58.920171 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 13:26:58.920192 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:26:58.920204 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:26:58.920217 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:26:58.920228 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 13:26:58.920240 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 13:26:58.920253 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 13:26:58.920265 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:26:58.920277 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:26:58.920289 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:26:58.920301 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:26:58.920312 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:26:58.920325 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:26:58.920336 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:26:58.920347 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:26:58.920359 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:26:58.920370 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:26:58.920382 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:26:58.920393 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:26:58.920404 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:26:58.920421 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:26:58.920447 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:26:58.920481 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.74431 (* 0.0454545 = 0.170196 loss) | |
| I0404 13:26:58.920506 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.00198 (* 0.0454545 = 0.181908 loss) | |
| I0404 13:26:58.920521 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.94759 (* 0.0454545 = 0.179436 loss) | |
| I0404 13:26:58.920536 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.92889 (* 0.0454545 = 0.178586 loss) | |
| I0404 13:26:58.920549 9252 solver.cpp:245] Train net output #26: loss/loss05 = 4.10236 (* 0.0454545 = 0.186471 loss) | |
| I0404 13:26:58.920563 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.47382 (* 0.0454545 = 0.112446 loss) | |
| I0404 13:26:58.920578 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.02299 (* 0.0454545 = 0.0464997 loss) | |
| I0404 13:26:58.920606 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.529286 (* 0.0454545 = 0.0240584 loss) | |
| I0404 13:26:58.920624 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0263209 (* 0.0454545 = 0.0011964 loss) | |
| I0404 13:26:58.920639 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0104502 (* 0.0454545 = 0.00047501 loss) | |
| I0404 13:26:58.920653 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.63385e-05 (* 0.0454545 = 3.01539e-06 loss) | |
| I0404 13:26:58.920667 9252 solver.cpp:245] Train net output #33: loss/loss12 = 6.27441e-05 (* 0.0454545 = 2.852e-06 loss) | |
| I0404 13:26:58.920681 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.59865e-05 (* 0.0454545 = 2.99939e-06 loss) | |
| I0404 13:26:58.920696 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.56604e-05 (* 0.0454545 = 2.98456e-06 loss) | |
| I0404 13:26:58.920711 9252 solver.cpp:245] Train net output #36: loss/loss15 = 6.04318e-05 (* 0.0454545 = 2.7469e-06 loss) | |
| I0404 13:26:58.920724 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.17996e-05 (* 0.0454545 = 2.80907e-06 loss) | |
| I0404 13:26:58.920738 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.30332e-05 (* 0.0454545 = 2.86515e-06 loss) | |
| I0404 13:26:58.920773 9252 solver.cpp:245] Train net output #39: loss/loss18 = 6.50938e-05 (* 0.0454545 = 2.95881e-06 loss) | |
| I0404 13:26:58.920789 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.34169e-05 (* 0.0454545 = 2.88259e-06 loss) | |
| I0404 13:26:58.920804 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.1032e-05 (* 0.0454545 = 2.77418e-06 loss) | |
| I0404 13:26:58.920817 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.13823e-05 (* 0.0454545 = 2.7901e-06 loss) | |
| I0404 13:26:58.920831 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.25301e-05 (* 0.0454545 = 2.84228e-06 loss) | |
| I0404 13:26:58.920845 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:26:58.920855 9252 solver.cpp:245] Train net output #45: total_confidence = 3.41628e-06 | |
| I0404 13:26:58.920869 9252 sgd_solver.cpp:106] Iteration 16000, lr = 0.00984 | |
| I0404 13:28:08.443783 9252 solver.cpp:229] Iteration 16500, loss = 1.08107 | |
| I0404 13:28:08.443898 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.15625 | |
| I0404 13:28:08.443918 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:28:08.443931 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:28:08.443943 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 13:28:08.443955 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.0625 | |
| I0404 13:28:08.443967 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.28125 | |
| I0404 13:28:08.443979 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 13:28:08.443990 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.8125 | |
| I0404 13:28:08.444002 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 13:28:08.444015 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.90625 | |
| I0404 13:28:08.444026 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:28:08.444038 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:28:08.444049 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:28:08.444061 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:28:08.444072 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:28:08.444083 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:28:08.444094 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:28:08.444106 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:28:08.444118 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:28:08.444129 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:28:08.444140 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:28:08.444152 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:28:08.444167 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.40853 (* 0.0454545 = 0.154933 loss) | |
| I0404 13:28:08.444182 9252 solver.cpp:245] Train net output #23: loss/loss02 = 4.23945 (* 0.0454545 = 0.192702 loss) | |
| I0404 13:28:08.444195 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.86415 (* 0.0454545 = 0.175643 loss) | |
| I0404 13:28:08.444210 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.81611 (* 0.0454545 = 0.17346 loss) | |
| I0404 13:28:08.444223 9252 solver.cpp:245] Train net output #26: loss/loss05 = 4.01561 (* 0.0454545 = 0.182528 loss) | |
| I0404 13:28:08.444236 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.17773 (* 0.0454545 = 0.144442 loss) | |
| I0404 13:28:08.444250 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.98605 (* 0.0454545 = 0.0902749 loss) | |
| I0404 13:28:08.444264 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.900662 (* 0.0454545 = 0.0409392 loss) | |
| I0404 13:28:08.444278 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.492929 (* 0.0454545 = 0.0224059 loss) | |
| I0404 13:28:08.444291 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.674552 (* 0.0454545 = 0.0306614 loss) | |
| I0404 13:28:08.444306 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000297544 (* 0.0454545 = 1.35247e-05 loss) | |
| I0404 13:28:08.444320 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000289701 (* 0.0454545 = 1.31682e-05 loss) | |
| I0404 13:28:08.444334 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000273817 (* 0.0454545 = 1.24462e-05 loss) | |
| I0404 13:28:08.444349 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000289065 (* 0.0454545 = 1.31393e-05 loss) | |
| I0404 13:28:08.444362 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000252069 (* 0.0454545 = 1.14577e-05 loss) | |
| I0404 13:28:08.444376 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000260678 (* 0.0454545 = 1.1849e-05 loss) | |
| I0404 13:28:08.444391 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000271936 (* 0.0454545 = 1.23607e-05 loss) | |
| I0404 13:28:08.444422 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000267648 (* 0.0454545 = 1.21658e-05 loss) | |
| I0404 13:28:08.444437 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00026724 (* 0.0454545 = 1.21473e-05 loss) | |
| I0404 13:28:08.444453 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000266819 (* 0.0454545 = 1.21281e-05 loss) | |
| I0404 13:28:08.444465 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000250269 (* 0.0454545 = 1.13759e-05 loss) | |
| I0404 13:28:08.444479 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000261218 (* 0.0454545 = 1.18736e-05 loss) | |
| I0404 13:28:08.444491 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:28:08.444504 9252 solver.cpp:245] Train net output #45: total_confidence = 2.41e-06 | |
| I0404 13:28:08.444519 9252 sgd_solver.cpp:106] Iteration 16500, lr = 0.009835 | |
| I0404 13:29:17.964442 9252 solver.cpp:229] Iteration 17000, loss = 1.06995 | |
| I0404 13:29:17.964576 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 13:29:17.964596 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:29:17.964608 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:29:17.964620 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:29:17.964632 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.125 | |
| I0404 13:29:17.964644 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.1875 | |
| I0404 13:29:17.964656 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:29:17.964668 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:29:17.964681 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 13:29:17.964694 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:29:17.964705 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:29:17.964717 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:29:17.964728 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:29:17.964740 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:29:17.964754 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:29:17.964766 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:29:17.964778 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:29:17.964790 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:29:17.964802 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:29:17.964813 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:29:17.964824 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:29:17.964836 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:29:17.964853 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.33643 (* 0.0454545 = 0.151656 loss) | |
| I0404 13:29:17.964866 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.78273 (* 0.0454545 = 0.171942 loss) | |
| I0404 13:29:17.964880 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.73011 (* 0.0454545 = 0.169551 loss) | |
| I0404 13:29:17.964895 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.70954 (* 0.0454545 = 0.168615 loss) | |
| I0404 13:29:17.964907 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.6692 (* 0.0454545 = 0.166782 loss) | |
| I0404 13:29:17.964920 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.34242 (* 0.0454545 = 0.151928 loss) | |
| I0404 13:29:17.964934 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.77922 (* 0.0454545 = 0.0808736 loss) | |
| I0404 13:29:17.964947 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.435982 (* 0.0454545 = 0.0198174 loss) | |
| I0404 13:29:17.964962 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.500351 (* 0.0454545 = 0.0227432 loss) | |
| I0404 13:29:17.964975 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0143578 (* 0.0454545 = 0.00065263 loss) | |
| I0404 13:29:17.964989 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000150053 (* 0.0454545 = 6.8206e-06 loss) | |
| I0404 13:29:17.965003 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000143866 (* 0.0454545 = 6.53936e-06 loss) | |
| I0404 13:29:17.965018 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000144688 (* 0.0454545 = 6.57673e-06 loss) | |
| I0404 13:29:17.965031 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000146563 (* 0.0454545 = 6.66194e-06 loss) | |
| I0404 13:29:17.965044 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000134793 (* 0.0454545 = 6.12697e-06 loss) | |
| I0404 13:29:17.965075 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000146435 (* 0.0454545 = 6.65615e-06 loss) | |
| I0404 13:29:17.965093 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000139206 (* 0.0454545 = 6.32754e-06 loss) | |
| I0404 13:29:17.965121 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000142767 (* 0.0454545 = 6.48941e-06 loss) | |
| I0404 13:29:17.965137 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000139927 (* 0.0454545 = 6.36033e-06 loss) | |
| I0404 13:29:17.965150 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000138008 (* 0.0454545 = 6.27309e-06 loss) | |
| I0404 13:29:17.965164 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000136102 (* 0.0454545 = 6.18647e-06 loss) | |
| I0404 13:29:17.965178 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000139092 (* 0.0454545 = 6.32237e-06 loss) | |
| I0404 13:29:17.965190 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:29:17.965201 9252 solver.cpp:245] Train net output #45: total_confidence = 7.36414e-06 | |
| I0404 13:29:17.965216 9252 sgd_solver.cpp:106] Iteration 17000, lr = 0.00983 | |
| I0404 13:30:27.395788 9252 solver.cpp:229] Iteration 17500, loss = 1.06461 | |
| I0404 13:30:27.395920 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:30:27.395943 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:30:27.395956 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:30:27.395968 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:30:27.395982 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 13:30:27.395992 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 13:30:27.396004 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 13:30:27.396016 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 13:30:27.396028 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:30:27.396040 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:30:27.396052 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:30:27.396064 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:30:27.396075 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:30:27.396086 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:30:27.396098 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:30:27.396109 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:30:27.396121 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:30:27.396132 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:30:27.396144 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:30:27.396155 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:30:27.396167 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:30:27.396178 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:30:27.396194 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.38942 (* 0.0454545 = 0.154065 loss) | |
| I0404 13:30:27.396209 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.62052 (* 0.0454545 = 0.164569 loss) | |
| I0404 13:30:27.396222 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.49871 (* 0.0454545 = 0.159032 loss) | |
| I0404 13:30:27.396236 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.83913 (* 0.0454545 = 0.174506 loss) | |
| I0404 13:30:27.396250 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.25366 (* 0.0454545 = 0.147894 loss) | |
| I0404 13:30:27.396263 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.26678 (* 0.0454545 = 0.103036 loss) | |
| I0404 13:30:27.396276 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.760197 (* 0.0454545 = 0.0345544 loss) | |
| I0404 13:30:27.396291 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.164748 (* 0.0454545 = 0.00748857 loss) | |
| I0404 13:30:27.396304 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0110677 (* 0.0454545 = 0.000503077 loss) | |
| I0404 13:30:27.396318 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00364844 (* 0.0454545 = 0.000165838 loss) | |
| I0404 13:30:27.396332 9252 solver.cpp:245] Train net output #32: loss/loss11 = 9.60034e-06 (* 0.0454545 = 4.36379e-07 loss) | |
| I0404 13:30:27.396347 9252 solver.cpp:245] Train net output #33: loss/loss12 = 8.6876e-06 (* 0.0454545 = 3.94891e-07 loss) | |
| I0404 13:30:27.396360 9252 solver.cpp:245] Train net output #34: loss/loss13 = 8.9968e-06 (* 0.0454545 = 4.08945e-07 loss) | |
| I0404 13:30:27.396374 9252 solver.cpp:245] Train net output #35: loss/loss14 = 8.82916e-06 (* 0.0454545 = 4.01326e-07 loss) | |
| I0404 13:30:27.396389 9252 solver.cpp:245] Train net output #36: loss/loss15 = 8.13994e-06 (* 0.0454545 = 3.69998e-07 loss) | |
| I0404 13:30:27.396402 9252 solver.cpp:245] Train net output #37: loss/loss16 = 9.03033e-06 (* 0.0454545 = 4.1047e-07 loss) | |
| I0404 13:30:27.396416 9252 solver.cpp:245] Train net output #38: loss/loss17 = 8.65034e-06 (* 0.0454545 = 3.93197e-07 loss) | |
| I0404 13:30:27.396447 9252 solver.cpp:245] Train net output #39: loss/loss18 = 8.93718e-06 (* 0.0454545 = 4.06235e-07 loss) | |
| I0404 13:30:27.396462 9252 solver.cpp:245] Train net output #40: loss/loss19 = 8.24798e-06 (* 0.0454545 = 3.74908e-07 loss) | |
| I0404 13:30:27.396476 9252 solver.cpp:245] Train net output #41: loss/loss20 = 8.29642e-06 (* 0.0454545 = 3.7711e-07 loss) | |
| I0404 13:30:27.396491 9252 solver.cpp:245] Train net output #42: loss/loss21 = 8.24053e-06 (* 0.0454545 = 3.74569e-07 loss) | |
| I0404 13:30:27.396504 9252 solver.cpp:245] Train net output #43: loss/loss22 = 8.33367e-06 (* 0.0454545 = 3.78803e-07 loss) | |
| I0404 13:30:27.396517 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:30:27.396528 9252 solver.cpp:245] Train net output #45: total_confidence = 1.97708e-05 | |
| I0404 13:30:27.396543 9252 sgd_solver.cpp:106] Iteration 17500, lr = 0.009825 | |
| I0404 13:31:36.749136 9252 solver.cpp:229] Iteration 18000, loss = 1.05319 | |
| I0404 13:31:36.749279 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:31:36.749299 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:31:36.749311 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:31:36.749323 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:31:36.749336 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 13:31:36.749347 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 13:31:36.749359 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 13:31:36.749372 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 13:31:36.749383 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:31:36.749394 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 13:31:36.749407 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:31:36.749431 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:31:36.749446 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:31:36.749457 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:31:36.749469 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:31:36.749480 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:31:36.749492 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:31:36.749503 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:31:36.749516 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:31:36.749526 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:31:36.749538 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:31:36.749549 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:31:36.749564 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.86709 (* 0.0454545 = 0.175777 loss) | |
| I0404 13:31:36.749579 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.75636 (* 0.0454545 = 0.170744 loss) | |
| I0404 13:31:36.749593 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.97217 (* 0.0454545 = 0.180553 loss) | |
| I0404 13:31:36.749606 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.71811 (* 0.0454545 = 0.169005 loss) | |
| I0404 13:31:36.749620 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.21973 (* 0.0454545 = 0.146352 loss) | |
| I0404 13:31:36.749634 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.17796 (* 0.0454545 = 0.0989981 loss) | |
| I0404 13:31:36.749646 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.39193 (* 0.0454545 = 0.0632697 loss) | |
| I0404 13:31:36.749660 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.741388 (* 0.0454545 = 0.0336994 loss) | |
| I0404 13:31:36.749673 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.342334 (* 0.0454545 = 0.0155606 loss) | |
| I0404 13:31:36.749687 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.403972 (* 0.0454545 = 0.0183623 loss) | |
| I0404 13:31:36.749701 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000265822 (* 0.0454545 = 1.20828e-05 loss) | |
| I0404 13:31:36.749716 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000264927 (* 0.0454545 = 1.20421e-05 loss) | |
| I0404 13:31:36.749729 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00026355 (* 0.0454545 = 1.19795e-05 loss) | |
| I0404 13:31:36.749743 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000265435 (* 0.0454545 = 1.20652e-05 loss) | |
| I0404 13:31:36.749758 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000258782 (* 0.0454545 = 1.17628e-05 loss) | |
| I0404 13:31:36.749771 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000258346 (* 0.0454545 = 1.1743e-05 loss) | |
| I0404 13:31:36.749785 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000259322 (* 0.0454545 = 1.17874e-05 loss) | |
| I0404 13:31:36.749817 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000249067 (* 0.0454545 = 1.13212e-05 loss) | |
| I0404 13:31:36.749832 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000252562 (* 0.0454545 = 1.14801e-05 loss) | |
| I0404 13:31:36.749846 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000261713 (* 0.0454545 = 1.1896e-05 loss) | |
| I0404 13:31:36.749861 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000259687 (* 0.0454545 = 1.18039e-05 loss) | |
| I0404 13:31:36.749874 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000263512 (* 0.0454545 = 1.19778e-05 loss) | |
| I0404 13:31:36.749886 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:31:36.749897 9252 solver.cpp:245] Train net output #45: total_confidence = 1.56876e-05 | |
| I0404 13:31:36.749915 9252 sgd_solver.cpp:106] Iteration 18000, lr = 0.00982 | |
| I0404 13:32:46.183591 9252 solver.cpp:229] Iteration 18500, loss = 1.05577 | |
| I0404 13:32:46.183714 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 13:32:46.183734 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:32:46.183749 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 13:32:46.183763 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 13:32:46.183774 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.125 | |
| I0404 13:32:46.183787 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.28125 | |
| I0404 13:32:46.183800 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:32:46.183811 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 13:32:46.183823 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:32:46.183835 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:32:46.183847 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:32:46.183858 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:32:46.183871 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:32:46.183881 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:32:46.183893 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:32:46.183904 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:32:46.183917 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:32:46.183928 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:32:46.183939 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:32:46.183950 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:32:46.183962 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:32:46.183974 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:32:46.183990 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.37339 (* 0.0454545 = 0.153336 loss) | |
| I0404 13:32:46.184005 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.68006 (* 0.0454545 = 0.167276 loss) | |
| I0404 13:32:46.184018 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.58372 (* 0.0454545 = 0.162897 loss) | |
| I0404 13:32:46.184031 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.80673 (* 0.0454545 = 0.173033 loss) | |
| I0404 13:32:46.184046 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.56261 (* 0.0454545 = 0.161937 loss) | |
| I0404 13:32:46.184059 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.08199 (* 0.0454545 = 0.14009 loss) | |
| I0404 13:32:46.184072 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.62206 (* 0.0454545 = 0.0737299 loss) | |
| I0404 13:32:46.184087 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.726503 (* 0.0454545 = 0.0330229 loss) | |
| I0404 13:32:46.184099 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.459396 (* 0.0454545 = 0.0208816 loss) | |
| I0404 13:32:46.184113 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.275913 (* 0.0454545 = 0.0125415 loss) | |
| I0404 13:32:46.184128 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000200788 (* 0.0454545 = 9.12674e-06 loss) | |
| I0404 13:32:46.184142 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000189522 (* 0.0454545 = 8.61462e-06 loss) | |
| I0404 13:32:46.184156 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000197275 (* 0.0454545 = 8.96704e-06 loss) | |
| I0404 13:32:46.184170 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000193477 (* 0.0454545 = 8.7944e-06 loss) | |
| I0404 13:32:46.184183 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000184998 (* 0.0454545 = 8.40899e-06 loss) | |
| I0404 13:32:46.184197 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000180213 (* 0.0454545 = 8.19148e-06 loss) | |
| I0404 13:32:46.184211 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000179003 (* 0.0454545 = 8.13651e-06 loss) | |
| I0404 13:32:46.184242 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.00019979 (* 0.0454545 = 9.08135e-06 loss) | |
| I0404 13:32:46.184258 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000174118 (* 0.0454545 = 7.91444e-06 loss) | |
| I0404 13:32:46.184273 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000173877 (* 0.0454545 = 7.90352e-06 loss) | |
| I0404 13:32:46.184286 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000180913 (* 0.0454545 = 8.2233e-06 loss) | |
| I0404 13:32:46.184300 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000178456 (* 0.0454545 = 8.11162e-06 loss) | |
| I0404 13:32:46.184311 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:32:46.184324 9252 solver.cpp:245] Train net output #45: total_confidence = 5.53919e-07 | |
| I0404 13:32:46.184337 9252 sgd_solver.cpp:106] Iteration 18500, lr = 0.009815 | |
| I0404 13:33:55.826742 9252 solver.cpp:229] Iteration 19000, loss = 1.03772 | |
| I0404 13:33:55.826884 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 13:33:55.826905 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:33:55.826917 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:33:55.826930 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:33:55.826942 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 13:33:55.826954 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.1875 | |
| I0404 13:33:55.826967 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.65625 | |
| I0404 13:33:55.826978 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 13:33:55.826990 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:33:55.827003 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:33:55.827013 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:33:55.827025 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:33:55.827036 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:33:55.827049 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:33:55.827059 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:33:55.827070 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:33:55.827082 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:33:55.827093 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:33:55.827105 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:33:55.827116 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:33:55.827127 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:33:55.827139 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:33:55.827155 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.47947 (* 0.0454545 = 0.158158 loss) | |
| I0404 13:33:55.827169 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.691 (* 0.0454545 = 0.167773 loss) | |
| I0404 13:33:55.827183 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.51552 (* 0.0454545 = 0.159796 loss) | |
| I0404 13:33:55.827196 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.67807 (* 0.0454545 = 0.167185 loss) | |
| I0404 13:33:55.827210 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.40461 (* 0.0454545 = 0.154755 loss) | |
| I0404 13:33:55.827224 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.66742 (* 0.0454545 = 0.166701 loss) | |
| I0404 13:33:55.827239 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.69654 (* 0.0454545 = 0.0771155 loss) | |
| I0404 13:33:55.827251 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.263937 (* 0.0454545 = 0.0119971 loss) | |
| I0404 13:33:55.827266 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0641288 (* 0.0454545 = 0.00291494 loss) | |
| I0404 13:33:55.827280 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0271025 (* 0.0454545 = 0.00123193 loss) | |
| I0404 13:33:55.827296 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.00048628 (* 0.0454545 = 2.21036e-05 loss) | |
| I0404 13:33:55.827309 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000531179 (* 0.0454545 = 2.41445e-05 loss) | |
| I0404 13:33:55.827323 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000459546 (* 0.0454545 = 2.08885e-05 loss) | |
| I0404 13:33:55.827337 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.00049505 (* 0.0454545 = 2.25023e-05 loss) | |
| I0404 13:33:55.827352 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000436892 (* 0.0454545 = 1.98587e-05 loss) | |
| I0404 13:33:55.827365 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000357168 (* 0.0454545 = 1.62349e-05 loss) | |
| I0404 13:33:55.827378 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000439465 (* 0.0454545 = 1.99757e-05 loss) | |
| I0404 13:33:55.827405 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000430021 (* 0.0454545 = 1.95464e-05 loss) | |
| I0404 13:33:55.827420 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000389075 (* 0.0454545 = 1.76852e-05 loss) | |
| I0404 13:33:55.827435 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000420739 (* 0.0454545 = 1.91245e-05 loss) | |
| I0404 13:33:55.827448 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000411863 (* 0.0454545 = 1.87211e-05 loss) | |
| I0404 13:33:55.827462 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000398689 (* 0.0454545 = 1.81222e-05 loss) | |
| I0404 13:33:55.827474 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:33:55.827486 9252 solver.cpp:245] Train net output #45: total_confidence = 1.2902e-05 | |
| I0404 13:33:55.827498 9252 sgd_solver.cpp:106] Iteration 19000, lr = 0.00981 | |
| I0404 13:35:05.454828 9252 solver.cpp:229] Iteration 19500, loss = 1.03065 | |
| I0404 13:35:05.454960 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 13:35:05.454982 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:35:05.454994 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 13:35:05.455006 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 13:35:05.455018 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 13:35:05.455031 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.3125 | |
| I0404 13:35:05.455042 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.59375 | |
| I0404 13:35:05.455055 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.75 | |
| I0404 13:35:05.455068 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 13:35:05.455080 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:35:05.455092 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:35:05.455104 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:35:05.455116 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:35:05.455127 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:35:05.455138 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:35:05.455150 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:35:05.455162 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:35:05.455173 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:35:05.455184 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:35:05.455195 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:35:05.455207 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:35:05.455219 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:35:05.455235 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.57133 (* 0.0454545 = 0.162333 loss) | |
| I0404 13:35:05.455248 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.83825 (* 0.0454545 = 0.174466 loss) | |
| I0404 13:35:05.455262 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.76557 (* 0.0454545 = 0.171162 loss) | |
| I0404 13:35:05.455276 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.61628 (* 0.0454545 = 0.164376 loss) | |
| I0404 13:35:05.455291 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.41324 (* 0.0454545 = 0.155147 loss) | |
| I0404 13:35:05.455303 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.0027 (* 0.0454545 = 0.136486 loss) | |
| I0404 13:35:05.455317 9252 solver.cpp:245] Train net output #28: loss/loss07 = 2.23564 (* 0.0454545 = 0.10162 loss) | |
| I0404 13:35:05.455332 9252 solver.cpp:245] Train net output #29: loss/loss08 = 1.333 (* 0.0454545 = 0.0605909 loss) | |
| I0404 13:35:05.455344 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.669283 (* 0.0454545 = 0.030422 loss) | |
| I0404 13:35:05.455358 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.304677 (* 0.0454545 = 0.0138489 loss) | |
| I0404 13:35:05.455373 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000305093 (* 0.0454545 = 1.38679e-05 loss) | |
| I0404 13:35:05.455386 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.00029926 (* 0.0454545 = 1.36027e-05 loss) | |
| I0404 13:35:05.455401 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000300476 (* 0.0454545 = 1.3658e-05 loss) | |
| I0404 13:35:05.455415 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000295205 (* 0.0454545 = 1.34184e-05 loss) | |
| I0404 13:35:05.455428 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000275384 (* 0.0454545 = 1.25175e-05 loss) | |
| I0404 13:35:05.455442 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000277125 (* 0.0454545 = 1.25966e-05 loss) | |
| I0404 13:35:05.455456 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.00027807 (* 0.0454545 = 1.26395e-05 loss) | |
| I0404 13:35:05.455487 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000303361 (* 0.0454545 = 1.37891e-05 loss) | |
| I0404 13:35:05.455503 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00026132 (* 0.0454545 = 1.18782e-05 loss) | |
| I0404 13:35:05.455518 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000261973 (* 0.0454545 = 1.19078e-05 loss) | |
| I0404 13:35:05.455531 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000264734 (* 0.0454545 = 1.20334e-05 loss) | |
| I0404 13:35:05.455544 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000267458 (* 0.0454545 = 1.21572e-05 loss) | |
| I0404 13:35:05.455556 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:35:05.455569 9252 solver.cpp:245] Train net output #45: total_confidence = 8.09051e-06 | |
| I0404 13:35:05.455581 9252 sgd_solver.cpp:106] Iteration 19500, lr = 0.009805 | |
| I0404 13:36:15.178531 9252 solver.cpp:338] Iteration 20000, Testing net (#0) | |
| I0404 13:36:23.168191 9252 solver.cpp:393] Test loss: 0.956876 | |
| I0404 13:36:23.168238 9252 solver.cpp:406] Test net output #0: loss/accuracy01 = 0.046 | |
| I0404 13:36:23.168253 9252 solver.cpp:406] Test net output #1: loss/accuracy02 = 0.083 | |
| I0404 13:36:23.168265 9252 solver.cpp:406] Test net output #2: loss/accuracy03 = 0.061 | |
| I0404 13:36:23.168277 9252 solver.cpp:406] Test net output #3: loss/accuracy04 = 0.082 | |
| I0404 13:36:23.168288 9252 solver.cpp:406] Test net output #4: loss/accuracy05 = 0.207 | |
| I0404 13:36:23.168300 9252 solver.cpp:406] Test net output #5: loss/accuracy06 = 0.493 | |
| I0404 13:36:23.168311 9252 solver.cpp:406] Test net output #6: loss/accuracy07 = 0.892 | |
| I0404 13:36:23.168323 9252 solver.cpp:406] Test net output #7: loss/accuracy08 = 0.97 | |
| I0404 13:36:23.168334 9252 solver.cpp:406] Test net output #8: loss/accuracy09 = 0.995 | |
| I0404 13:36:23.168345 9252 solver.cpp:406] Test net output #9: loss/accuracy10 = 0.998 | |
| I0404 13:36:23.168356 9252 solver.cpp:406] Test net output #10: loss/accuracy11 = 1 | |
| I0404 13:36:23.168367 9252 solver.cpp:406] Test net output #11: loss/accuracy12 = 1 | |
| I0404 13:36:23.168378 9252 solver.cpp:406] Test net output #12: loss/accuracy13 = 1 | |
| I0404 13:36:23.168390 9252 solver.cpp:406] Test net output #13: loss/accuracy14 = 1 | |
| I0404 13:36:23.168401 9252 solver.cpp:406] Test net output #14: loss/accuracy15 = 1 | |
| I0404 13:36:23.168411 9252 solver.cpp:406] Test net output #15: loss/accuracy16 = 1 | |
| I0404 13:36:23.168422 9252 solver.cpp:406] Test net output #16: loss/accuracy17 = 1 | |
| I0404 13:36:23.168433 9252 solver.cpp:406] Test net output #17: loss/accuracy18 = 1 | |
| I0404 13:36:23.168444 9252 solver.cpp:406] Test net output #18: loss/accuracy19 = 1 | |
| I0404 13:36:23.168455 9252 solver.cpp:406] Test net output #19: loss/accuracy20 = 1 | |
| I0404 13:36:23.168467 9252 solver.cpp:406] Test net output #20: loss/accuracy21 = 1 | |
| I0404 13:36:23.168478 9252 solver.cpp:406] Test net output #21: loss/accuracy22 = 1 | |
| I0404 13:36:23.168493 9252 solver.cpp:406] Test net output #22: loss/loss01 = 3.89169 (* 0.0454545 = 0.176895 loss) | |
| I0404 13:36:23.168508 9252 solver.cpp:406] Test net output #23: loss/loss02 = 3.26303 (* 0.0454545 = 0.14832 loss) | |
| I0404 13:36:23.168520 9252 solver.cpp:406] Test net output #24: loss/loss03 = 3.47303 (* 0.0454545 = 0.157865 loss) | |
| I0404 13:36:23.168534 9252 solver.cpp:406] Test net output #25: loss/loss04 = 3.56633 (* 0.0454545 = 0.162106 loss) | |
| I0404 13:36:23.168546 9252 solver.cpp:406] Test net output #26: loss/loss05 = 3.42151 (* 0.0454545 = 0.155523 loss) | |
| I0404 13:36:23.168560 9252 solver.cpp:406] Test net output #27: loss/loss06 = 2.33894 (* 0.0454545 = 0.106315 loss) | |
| I0404 13:36:23.168573 9252 solver.cpp:406] Test net output #28: loss/loss07 = 0.772331 (* 0.0454545 = 0.035106 loss) | |
| I0404 13:36:23.168586 9252 solver.cpp:406] Test net output #29: loss/loss08 = 0.249701 (* 0.0454545 = 0.0113501 loss) | |
| I0404 13:36:23.168601 9252 solver.cpp:406] Test net output #30: loss/loss09 = 0.0483566 (* 0.0454545 = 0.00219803 loss) | |
| I0404 13:36:23.168614 9252 solver.cpp:406] Test net output #31: loss/loss10 = 0.0233842 (* 0.0454545 = 0.00106292 loss) | |
| I0404 13:36:23.168628 9252 solver.cpp:406] Test net output #32: loss/loss11 = 0.000267762 (* 0.0454545 = 1.2171e-05 loss) | |
| I0404 13:36:23.168642 9252 solver.cpp:406] Test net output #33: loss/loss12 = 0.000261662 (* 0.0454545 = 1.18937e-05 loss) | |
| I0404 13:36:23.168655 9252 solver.cpp:406] Test net output #34: loss/loss13 = 0.000262206 (* 0.0454545 = 1.19184e-05 loss) | |
| I0404 13:36:23.168669 9252 solver.cpp:406] Test net output #35: loss/loss14 = 0.000251317 (* 0.0454545 = 1.14235e-05 loss) | |
| I0404 13:36:23.168683 9252 solver.cpp:406] Test net output #36: loss/loss15 = 0.000258543 (* 0.0454545 = 1.17519e-05 loss) | |
| I0404 13:36:23.168696 9252 solver.cpp:406] Test net output #37: loss/loss16 = 0.000226954 (* 0.0454545 = 1.03161e-05 loss) | |
| I0404 13:36:23.168710 9252 solver.cpp:406] Test net output #38: loss/loss17 = 0.000253685 (* 0.0454545 = 1.15311e-05 loss) | |
| I0404 13:36:23.168763 9252 solver.cpp:406] Test net output #39: loss/loss18 = 0.000255587 (* 0.0454545 = 1.16176e-05 loss) | |
| I0404 13:36:23.168778 9252 solver.cpp:406] Test net output #40: loss/loss19 = 0.000228811 (* 0.0454545 = 1.04005e-05 loss) | |
| I0404 13:36:23.168792 9252 solver.cpp:406] Test net output #41: loss/loss20 = 0.000238435 (* 0.0454545 = 1.08379e-05 loss) | |
| I0404 13:36:23.168805 9252 solver.cpp:406] Test net output #42: loss/loss21 = 0.00023415 (* 0.0454545 = 1.06432e-05 loss) | |
| I0404 13:36:23.168818 9252 solver.cpp:406] Test net output #43: loss/loss22 = 0.000242654 (* 0.0454545 = 1.10297e-05 loss) | |
| I0404 13:36:23.168830 9252 solver.cpp:406] Test net output #44: total_accuracy = 0 | |
| I0404 13:36:23.168841 9252 solver.cpp:406] Test net output #45: total_confidence = 6.47123e-06 | |
| I0404 13:36:23.202713 9252 solver.cpp:229] Iteration 20000, loss = 1.02878 | |
| I0404 13:36:23.202742 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:36:23.202756 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:36:23.202769 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 13:36:23.202781 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 13:36:23.202793 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 13:36:23.202805 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 13:36:23.202816 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 13:36:23.202827 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:36:23.202839 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:36:23.202852 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:36:23.202862 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:36:23.202873 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:36:23.202884 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:36:23.202895 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:36:23.202910 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:36:23.202921 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:36:23.202932 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:36:23.202944 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:36:23.202955 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:36:23.202965 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:36:23.202976 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:36:23.202987 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:36:23.203002 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.54059 (* 0.0454545 = 0.160936 loss) | |
| I0404 13:36:23.203016 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.71274 (* 0.0454545 = 0.168761 loss) | |
| I0404 13:36:23.203029 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.48047 (* 0.0454545 = 0.158203 loss) | |
| I0404 13:36:23.203043 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.6769 (* 0.0454545 = 0.167132 loss) | |
| I0404 13:36:23.203057 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.58069 (* 0.0454545 = 0.162758 loss) | |
| I0404 13:36:23.203069 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.66793 (* 0.0454545 = 0.12127 loss) | |
| I0404 13:36:23.203083 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.26487 (* 0.0454545 = 0.0574939 loss) | |
| I0404 13:36:23.203096 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.522894 (* 0.0454545 = 0.0237679 loss) | |
| I0404 13:36:23.203109 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.20231 (* 0.0454545 = 0.00919591 loss) | |
| I0404 13:36:23.203124 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00473654 (* 0.0454545 = 0.000215297 loss) | |
| I0404 13:36:23.203155 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000137484 (* 0.0454545 = 6.24928e-06 loss) | |
| I0404 13:36:23.203169 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000144687 (* 0.0454545 = 6.57667e-06 loss) | |
| I0404 13:36:23.203183 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00013156 (* 0.0454545 = 5.97998e-06 loss) | |
| I0404 13:36:23.203197 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000128796 (* 0.0454545 = 5.85437e-06 loss) | |
| I0404 13:36:23.203210 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000121901 (* 0.0454545 = 5.54097e-06 loss) | |
| I0404 13:36:23.203224 9252 solver.cpp:245] Train net output #37: loss/loss16 = 9.76667e-05 (* 0.0454545 = 4.4394e-06 loss) | |
| I0404 13:36:23.203238 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000121563 (* 0.0454545 = 5.52557e-06 loss) | |
| I0404 13:36:23.203251 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000119985 (* 0.0454545 = 5.45388e-06 loss) | |
| I0404 13:36:23.203265 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000102736 (* 0.0454545 = 4.66981e-06 loss) | |
| I0404 13:36:23.203279 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.0001135 (* 0.0454545 = 5.15911e-06 loss) | |
| I0404 13:36:23.203292 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000110028 (* 0.0454545 = 5.00127e-06 loss) | |
| I0404 13:36:23.203305 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000111114 (* 0.0454545 = 5.05063e-06 loss) | |
| I0404 13:36:23.203317 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:36:23.203328 9252 solver.cpp:245] Train net output #45: total_confidence = 6.00675e-06 | |
| I0404 13:36:23.203343 9252 sgd_solver.cpp:106] Iteration 20000, lr = 0.0098 | |
| I0404 13:37:31.894207 9252 solver.cpp:229] Iteration 20500, loss = 1.01736 | |
| I0404 13:37:31.894373 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 13:37:31.894393 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 13:37:31.894407 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 13:37:31.894419 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:37:31.894431 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 13:37:31.894443 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.3125 | |
| I0404 13:37:31.894455 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 13:37:31.894467 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.8125 | |
| I0404 13:37:31.894479 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:37:31.894491 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:37:31.894502 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:37:31.894515 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:37:31.894526 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:37:31.894536 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:37:31.894548 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:37:31.894559 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:37:31.894570 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:37:31.894582 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:37:31.894593 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:37:31.894604 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:37:31.894615 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:37:31.894628 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:37:31.894642 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.31729 (* 0.0454545 = 0.150786 loss) | |
| I0404 13:37:31.894657 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.42433 (* 0.0454545 = 0.155651 loss) | |
| I0404 13:37:31.894670 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.42598 (* 0.0454545 = 0.155726 loss) | |
| I0404 13:37:31.894685 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.81041 (* 0.0454545 = 0.173201 loss) | |
| I0404 13:37:31.894698 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.18402 (* 0.0454545 = 0.144728 loss) | |
| I0404 13:37:31.894711 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.74053 (* 0.0454545 = 0.124569 loss) | |
| I0404 13:37:31.894726 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.49831 (* 0.0454545 = 0.068105 loss) | |
| I0404 13:37:31.894738 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.948654 (* 0.0454545 = 0.0431206 loss) | |
| I0404 13:37:31.894757 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.204639 (* 0.0454545 = 0.00930176 loss) | |
| I0404 13:37:31.894770 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00999857 (* 0.0454545 = 0.00045448 loss) | |
| I0404 13:37:31.894785 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000168211 (* 0.0454545 = 7.64597e-06 loss) | |
| I0404 13:37:31.894799 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000179524 (* 0.0454545 = 8.16016e-06 loss) | |
| I0404 13:37:31.894814 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000168623 (* 0.0454545 = 7.66466e-06 loss) | |
| I0404 13:37:31.894827 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.0001497 (* 0.0454545 = 6.80457e-06 loss) | |
| I0404 13:37:31.894841 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000155247 (* 0.0454545 = 7.05669e-06 loss) | |
| I0404 13:37:31.894855 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000135158 (* 0.0454545 = 6.14356e-06 loss) | |
| I0404 13:37:31.894868 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000143634 (* 0.0454545 = 6.5288e-06 loss) | |
| I0404 13:37:31.894896 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000160847 (* 0.0454545 = 7.31124e-06 loss) | |
| I0404 13:37:31.894912 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000140827 (* 0.0454545 = 6.40124e-06 loss) | |
| I0404 13:37:31.894925 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000140181 (* 0.0454545 = 6.37185e-06 loss) | |
| I0404 13:37:31.894939 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00013439 (* 0.0454545 = 6.10864e-06 loss) | |
| I0404 13:37:31.894953 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000138353 (* 0.0454545 = 6.28879e-06 loss) | |
| I0404 13:37:31.894965 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:37:31.894978 9252 solver.cpp:245] Train net output #45: total_confidence = 3.27762e-05 | |
| I0404 13:37:31.894991 9252 sgd_solver.cpp:106] Iteration 20500, lr = 0.009795 | |
| I0404 13:38:41.881062 9252 solver.cpp:229] Iteration 21000, loss = 1.00233 | |
| I0404 13:38:41.881181 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 13:38:41.881201 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 13:38:41.881214 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:38:41.881225 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:38:41.881237 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 13:38:41.881249 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 13:38:41.881261 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 13:38:41.881273 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 13:38:41.881285 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:38:41.881297 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:38:41.881309 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:38:41.881320 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:38:41.881332 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:38:41.881345 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:38:41.881356 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:38:41.881367 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:38:41.881379 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:38:41.881392 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:38:41.881402 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:38:41.881414 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:38:41.881439 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:38:41.881453 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:38:41.881469 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.33481 (* 0.0454545 = 0.151582 loss) | |
| I0404 13:38:41.881482 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.3963 (* 0.0454545 = 0.154377 loss) | |
| I0404 13:38:41.881505 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.30166 (* 0.0454545 = 0.150076 loss) | |
| I0404 13:38:41.881520 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.38041 (* 0.0454545 = 0.153655 loss) | |
| I0404 13:38:41.881532 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.11273 (* 0.0454545 = 0.141488 loss) | |
| I0404 13:38:41.881546 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.51541 (* 0.0454545 = 0.114337 loss) | |
| I0404 13:38:41.881561 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.40871 (* 0.0454545 = 0.0640321 loss) | |
| I0404 13:38:41.881574 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.132423 (* 0.0454545 = 0.00601922 loss) | |
| I0404 13:38:41.881588 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.120278 (* 0.0454545 = 0.00546717 loss) | |
| I0404 13:38:41.881603 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0109603 (* 0.0454545 = 0.000498193 loss) | |
| I0404 13:38:41.881618 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.93874e-05 (* 0.0454545 = 8.81248e-07 loss) | |
| I0404 13:38:41.881631 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.86779e-05 (* 0.0454545 = 8.48994e-07 loss) | |
| I0404 13:38:41.881645 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.83965e-05 (* 0.0454545 = 8.36203e-07 loss) | |
| I0404 13:38:41.881659 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.83219e-05 (* 0.0454545 = 8.32815e-07 loss) | |
| I0404 13:38:41.881674 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.73309e-05 (* 0.0454545 = 7.87767e-07 loss) | |
| I0404 13:38:41.881687 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.49241e-05 (* 0.0454545 = 6.78367e-07 loss) | |
| I0404 13:38:41.881701 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.72042e-05 (* 0.0454545 = 7.8201e-07 loss) | |
| I0404 13:38:41.881733 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.81207e-05 (* 0.0454545 = 8.23667e-07 loss) | |
| I0404 13:38:41.881748 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.58369e-05 (* 0.0454545 = 7.19858e-07 loss) | |
| I0404 13:38:41.881762 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.52743e-05 (* 0.0454545 = 6.94288e-07 loss) | |
| I0404 13:38:41.881777 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.57624e-05 (* 0.0454545 = 7.16472e-07 loss) | |
| I0404 13:38:41.881790 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.59524e-05 (* 0.0454545 = 7.25108e-07 loss) | |
| I0404 13:38:41.881803 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:38:41.881814 9252 solver.cpp:245] Train net output #45: total_confidence = 2.79784e-05 | |
| I0404 13:38:41.881829 9252 sgd_solver.cpp:106] Iteration 21000, lr = 0.00979 | |
| I0404 13:39:52.326408 9252 solver.cpp:229] Iteration 21500, loss = 0.998318 | |
| I0404 13:39:52.326535 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.03125 | |
| I0404 13:39:52.326555 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:39:52.326568 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 13:39:52.326581 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 13:39:52.326592 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 13:39:52.326603 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 13:39:52.326616 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 13:39:52.326627 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:39:52.326639 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:39:52.326652 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:39:52.326663 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:39:52.326674 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:39:52.326686 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:39:52.326699 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:39:52.326709 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:39:52.326721 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:39:52.326733 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:39:52.326747 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:39:52.326759 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:39:52.326771 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:39:52.326782 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:39:52.326793 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:39:52.326809 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.68444 (* 0.0454545 = 0.167475 loss) | |
| I0404 13:39:52.326823 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.43529 (* 0.0454545 = 0.15615 loss) | |
| I0404 13:39:52.326838 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.36284 (* 0.0454545 = 0.152856 loss) | |
| I0404 13:39:52.326850 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.48069 (* 0.0454545 = 0.158213 loss) | |
| I0404 13:39:52.326864 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.21935 (* 0.0454545 = 0.146334 loss) | |
| I0404 13:39:52.326877 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.93908 (* 0.0454545 = 0.133595 loss) | |
| I0404 13:39:52.326891 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.47607 (* 0.0454545 = 0.0670943 loss) | |
| I0404 13:39:52.326905 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.494329 (* 0.0454545 = 0.0224695 loss) | |
| I0404 13:39:52.326918 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.293653 (* 0.0454545 = 0.0133479 loss) | |
| I0404 13:39:52.326931 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.210149 (* 0.0454545 = 0.00955224 loss) | |
| I0404 13:39:52.326946 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.8346e-05 (* 0.0454545 = 1.743e-06 loss) | |
| I0404 13:39:52.326961 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.06811e-05 (* 0.0454545 = 1.84914e-06 loss) | |
| I0404 13:39:52.326974 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.18708e-05 (* 0.0454545 = 1.90322e-06 loss) | |
| I0404 13:39:52.326987 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.88737e-05 (* 0.0454545 = 1.76699e-06 loss) | |
| I0404 13:39:52.327002 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.93715e-05 (* 0.0454545 = 1.78961e-06 loss) | |
| I0404 13:39:52.327015 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.78663e-05 (* 0.0454545 = 1.26665e-06 loss) | |
| I0404 13:39:52.327029 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.69113e-05 (* 0.0454545 = 1.67779e-06 loss) | |
| I0404 13:39:52.327060 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.90672e-05 (* 0.0454545 = 1.77578e-06 loss) | |
| I0404 13:39:52.327075 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.21522e-05 (* 0.0454545 = 1.46146e-06 loss) | |
| I0404 13:39:52.327090 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.45194e-05 (* 0.0454545 = 1.56906e-06 loss) | |
| I0404 13:39:52.327102 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.64593e-05 (* 0.0454545 = 1.65724e-06 loss) | |
| I0404 13:39:52.327116 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.46685e-05 (* 0.0454545 = 1.57584e-06 loss) | |
| I0404 13:39:52.327127 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:39:52.327138 9252 solver.cpp:245] Train net output #45: total_confidence = 4.17923e-05 | |
| I0404 13:39:52.327154 9252 sgd_solver.cpp:106] Iteration 21500, lr = 0.009785 | |
| I0404 13:41:01.444106 9252 solver.cpp:229] Iteration 22000, loss = 0.993056 | |
| I0404 13:41:01.444248 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:41:01.444267 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:41:01.444280 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:41:01.444293 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:41:01.444304 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 13:41:01.444316 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 13:41:01.444329 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 13:41:01.444341 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 13:41:01.444353 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:41:01.444365 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:41:01.444376 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:41:01.444388 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:41:01.444401 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:41:01.444411 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:41:01.444423 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:41:01.444435 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:41:01.444447 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:41:01.444458 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:41:01.444470 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:41:01.444485 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:41:01.444497 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:41:01.444509 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:41:01.444525 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.30558 (* 0.0454545 = 0.150254 loss) | |
| I0404 13:41:01.444540 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.20142 (* 0.0454545 = 0.145519 loss) | |
| I0404 13:41:01.444553 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.26899 (* 0.0454545 = 0.14859 loss) | |
| I0404 13:41:01.444566 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.29852 (* 0.0454545 = 0.149933 loss) | |
| I0404 13:41:01.444581 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.9967 (* 0.0454545 = 0.136214 loss) | |
| I0404 13:41:01.444594 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.37543 (* 0.0454545 = 0.107974 loss) | |
| I0404 13:41:01.444608 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.854756 (* 0.0454545 = 0.0388525 loss) | |
| I0404 13:41:01.444622 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.31561 (* 0.0454545 = 0.0143459 loss) | |
| I0404 13:41:01.444636 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.222345 (* 0.0454545 = 0.0101066 loss) | |
| I0404 13:41:01.444651 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0500269 (* 0.0454545 = 0.00227395 loss) | |
| I0404 13:41:01.444665 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000871894 (* 0.0454545 = 3.96315e-05 loss) | |
| I0404 13:41:01.444680 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.00085234 (* 0.0454545 = 3.87427e-05 loss) | |
| I0404 13:41:01.444694 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000925375 (* 0.0454545 = 4.20625e-05 loss) | |
| I0404 13:41:01.444708 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000783699 (* 0.0454545 = 3.56227e-05 loss) | |
| I0404 13:41:01.444722 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000957139 (* 0.0454545 = 4.35063e-05 loss) | |
| I0404 13:41:01.444736 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000707195 (* 0.0454545 = 3.21452e-05 loss) | |
| I0404 13:41:01.444751 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000802656 (* 0.0454545 = 3.64844e-05 loss) | |
| I0404 13:41:01.444780 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000928867 (* 0.0454545 = 4.22212e-05 loss) | |
| I0404 13:41:01.444797 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000767299 (* 0.0454545 = 3.48772e-05 loss) | |
| I0404 13:41:01.444810 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000850336 (* 0.0454545 = 3.86516e-05 loss) | |
| I0404 13:41:01.444824 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000835554 (* 0.0454545 = 3.79797e-05 loss) | |
| I0404 13:41:01.444839 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.0008131 (* 0.0454545 = 3.69591e-05 loss) | |
| I0404 13:41:01.444850 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:41:01.444862 9252 solver.cpp:245] Train net output #45: total_confidence = 1.56013e-05 | |
| I0404 13:41:01.444875 9252 sgd_solver.cpp:106] Iteration 22000, lr = 0.00978 | |
| I0404 13:42:11.385491 9252 solver.cpp:229] Iteration 22500, loss = 0.985664 | |
| I0404 13:42:11.385639 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 13:42:11.385660 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 13:42:11.385673 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:42:11.385685 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 13:42:11.385697 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 13:42:11.385710 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.28125 | |
| I0404 13:42:11.385722 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.65625 | |
| I0404 13:42:11.385735 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 13:42:11.385746 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:42:11.385757 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:42:11.385769 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:42:11.385784 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:42:11.385804 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:42:11.385818 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:42:11.385830 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:42:11.385841 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:42:11.385854 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:42:11.385864 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:42:11.385875 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:42:11.385887 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:42:11.385901 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:42:11.385913 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:42:11.385929 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.24572 (* 0.0454545 = 0.147533 loss) | |
| I0404 13:42:11.385943 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.25764 (* 0.0454545 = 0.148075 loss) | |
| I0404 13:42:11.385957 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.4231 (* 0.0454545 = 0.155596 loss) | |
| I0404 13:42:11.385972 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.20309 (* 0.0454545 = 0.145595 loss) | |
| I0404 13:42:11.385985 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.48258 (* 0.0454545 = 0.158299 loss) | |
| I0404 13:42:11.385998 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.86418 (* 0.0454545 = 0.13019 loss) | |
| I0404 13:42:11.386013 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.59346 (* 0.0454545 = 0.07243 loss) | |
| I0404 13:42:11.386025 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.8726 (* 0.0454545 = 0.0396636 loss) | |
| I0404 13:42:11.386039 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.379897 (* 0.0454545 = 0.017268 loss) | |
| I0404 13:42:11.386052 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.390846 (* 0.0454545 = 0.0177657 loss) | |
| I0404 13:42:11.386066 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000101894 (* 0.0454545 = 4.63153e-06 loss) | |
| I0404 13:42:11.386101 9252 solver.cpp:245] Train net output #33: loss/loss12 = 9.75617e-05 (* 0.0454545 = 4.43462e-06 loss) | |
| I0404 13:42:11.386117 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000101508 (* 0.0454545 = 4.614e-06 loss) | |
| I0404 13:42:11.386135 9252 solver.cpp:245] Train net output #35: loss/loss14 = 9.57596e-05 (* 0.0454545 = 4.35271e-06 loss) | |
| I0404 13:42:11.386158 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000103605 (* 0.0454545 = 4.70931e-06 loss) | |
| I0404 13:42:11.386173 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.78579e-05 (* 0.0454545 = 2.62991e-06 loss) | |
| I0404 13:42:11.386188 9252 solver.cpp:245] Train net output #38: loss/loss17 = 8.69167e-05 (* 0.0454545 = 3.95076e-06 loss) | |
| I0404 13:42:11.386216 9252 solver.cpp:245] Train net output #39: loss/loss18 = 9.64261e-05 (* 0.0454545 = 4.383e-06 loss) | |
| I0404 13:42:11.386231 9252 solver.cpp:245] Train net output #40: loss/loss19 = 7.80228e-05 (* 0.0454545 = 3.54649e-06 loss) | |
| I0404 13:42:11.386245 9252 solver.cpp:245] Train net output #41: loss/loss20 = 8.26734e-05 (* 0.0454545 = 3.75788e-06 loss) | |
| I0404 13:42:11.386260 9252 solver.cpp:245] Train net output #42: loss/loss21 = 8.31701e-05 (* 0.0454545 = 3.78046e-06 loss) | |
| I0404 13:42:11.386272 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.21372e-05 (* 0.0454545 = 3.27896e-06 loss) | |
| I0404 13:42:11.386284 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:42:11.386296 9252 solver.cpp:245] Train net output #45: total_confidence = 1.09228e-05 | |
| I0404 13:42:11.386309 9252 sgd_solver.cpp:106] Iteration 22500, lr = 0.009775 | |
| I0404 13:43:21.560844 9252 solver.cpp:229] Iteration 23000, loss = 0.984105 | |
| I0404 13:43:21.560959 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 13:43:21.560989 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 13:43:21.561015 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 13:43:21.561038 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:43:21.561063 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 13:43:21.561085 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 13:43:21.561110 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:43:21.561134 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 13:43:21.561158 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:43:21.561180 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:43:21.561203 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:43:21.561223 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:43:21.561265 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:43:21.561287 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:43:21.561316 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:43:21.561337 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:43:21.561358 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:43:21.561379 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:43:21.561401 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:43:21.561442 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:43:21.561470 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:43:21.561491 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:43:21.561520 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.04457 (* 0.0454545 = 0.13839 loss) | |
| I0404 13:43:21.561547 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.2875 (* 0.0454545 = 0.149432 loss) | |
| I0404 13:43:21.561580 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.68485 (* 0.0454545 = 0.167493 loss) | |
| I0404 13:43:21.561611 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.48578 (* 0.0454545 = 0.158445 loss) | |
| I0404 13:43:21.561640 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.4089 (* 0.0454545 = 0.15495 loss) | |
| I0404 13:43:21.561686 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.54069 (* 0.0454545 = 0.115486 loss) | |
| I0404 13:43:21.561712 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.27351 (* 0.0454545 = 0.0578869 loss) | |
| I0404 13:43:21.561746 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.705652 (* 0.0454545 = 0.0320751 loss) | |
| I0404 13:43:21.561774 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0604294 (* 0.0454545 = 0.00274679 loss) | |
| I0404 13:43:21.561802 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.029981 (* 0.0454545 = 0.00136277 loss) | |
| I0404 13:43:21.561830 9252 solver.cpp:245] Train net output #32: loss/loss11 = 8.03328e-05 (* 0.0454545 = 3.65149e-06 loss) | |
| I0404 13:43:21.561856 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.37723e-05 (* 0.0454545 = 3.35329e-06 loss) | |
| I0404 13:43:21.561883 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.85255e-05 (* 0.0454545 = 3.56934e-06 loss) | |
| I0404 13:43:21.561909 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.58934e-05 (* 0.0454545 = 3.4497e-06 loss) | |
| I0404 13:43:21.561936 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.34467e-05 (* 0.0454545 = 3.33849e-06 loss) | |
| I0404 13:43:21.561966 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.5126e-05 (* 0.0454545 = 2.96027e-06 loss) | |
| I0404 13:43:21.561995 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.81709e-05 (* 0.0454545 = 3.09868e-06 loss) | |
| I0404 13:43:21.562047 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.92773e-05 (* 0.0454545 = 3.60351e-06 loss) | |
| I0404 13:43:21.562075 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.73398e-05 (* 0.0454545 = 3.0609e-06 loss) | |
| I0404 13:43:21.562103 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.39964e-05 (* 0.0454545 = 2.90893e-06 loss) | |
| I0404 13:43:21.562129 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.81948e-05 (* 0.0454545 = 3.09977e-06 loss) | |
| I0404 13:43:21.562156 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.27725e-05 (* 0.0454545 = 2.8533e-06 loss) | |
| I0404 13:43:21.562178 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:43:21.562201 9252 solver.cpp:245] Train net output #45: total_confidence = 1.29328e-05 | |
| I0404 13:43:21.562224 9252 sgd_solver.cpp:106] Iteration 23000, lr = 0.00977 | |
| I0404 13:44:32.819851 9252 solver.cpp:229] Iteration 23500, loss = 0.97814 | |
| I0404 13:44:32.819982 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 13:44:32.820010 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:44:32.820035 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 13:44:32.820058 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 13:44:32.820080 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 13:44:32.820099 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 13:44:32.820117 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 13:44:32.820135 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 13:44:32.820157 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:44:32.820178 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 13:44:32.820201 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:44:32.820226 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:44:32.820248 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:44:32.820269 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:44:32.820291 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:44:32.820330 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:44:32.820353 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:44:32.820374 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:44:32.820404 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:44:32.820425 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:44:32.820444 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:44:32.820466 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:44:32.820493 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.23752 (* 0.0454545 = 0.14716 loss) | |
| I0404 13:44:32.820519 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.44055 (* 0.0454545 = 0.156389 loss) | |
| I0404 13:44:32.820545 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.58932 (* 0.0454545 = 0.163151 loss) | |
| I0404 13:44:32.820571 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.52136 (* 0.0454545 = 0.160062 loss) | |
| I0404 13:44:32.820597 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.69421 (* 0.0454545 = 0.167919 loss) | |
| I0404 13:44:32.820623 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.6077 (* 0.0454545 = 0.118532 loss) | |
| I0404 13:44:32.820648 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.991365 (* 0.0454545 = 0.0450621 loss) | |
| I0404 13:44:32.820674 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.598521 (* 0.0454545 = 0.0272055 loss) | |
| I0404 13:44:32.820700 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.342959 (* 0.0454545 = 0.0155891 loss) | |
| I0404 13:44:32.820725 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.325659 (* 0.0454545 = 0.0148027 loss) | |
| I0404 13:44:32.820752 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.18263e-05 (* 0.0454545 = 2.81029e-06 loss) | |
| I0404 13:44:32.820780 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.53435e-05 (* 0.0454545 = 2.51561e-06 loss) | |
| I0404 13:44:32.820806 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.91727e-05 (* 0.0454545 = 2.68967e-06 loss) | |
| I0404 13:44:32.820832 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.59993e-05 (* 0.0454545 = 2.54542e-06 loss) | |
| I0404 13:44:32.820858 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.61056e-05 (* 0.0454545 = 2.55025e-06 loss) | |
| I0404 13:44:32.820891 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.52297e-05 (* 0.0454545 = 2.51044e-06 loss) | |
| I0404 13:44:32.820922 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.97261e-05 (* 0.0454545 = 2.26028e-06 loss) | |
| I0404 13:44:32.820992 9252 solver.cpp:245] Train net output #39: loss/loss18 = 6.43758e-05 (* 0.0454545 = 2.92617e-06 loss) | |
| I0404 13:44:32.821032 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.53367e-05 (* 0.0454545 = 2.5153e-06 loss) | |
| I0404 13:44:32.821058 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.81775e-05 (* 0.0454545 = 2.18989e-06 loss) | |
| I0404 13:44:32.821084 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.28562e-05 (* 0.0454545 = 2.40255e-06 loss) | |
| I0404 13:44:32.821110 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.69682e-05 (* 0.0454545 = 2.13492e-06 loss) | |
| I0404 13:44:32.821132 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:44:32.821153 9252 solver.cpp:245] Train net output #45: total_confidence = 2.82552e-05 | |
| I0404 13:44:32.821176 9252 sgd_solver.cpp:106] Iteration 23500, lr = 0.009765 | |
| I0404 13:45:43.281571 9252 solver.cpp:229] Iteration 24000, loss = 0.975249 | |
| I0404 13:45:43.281688 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:45:43.281719 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:45:43.281745 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 13:45:43.281769 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:45:43.281793 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 13:45:43.281817 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 13:45:43.281843 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 13:45:43.281867 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:45:43.281889 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:45:43.281932 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:45:43.281957 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:45:43.281980 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:45:43.282009 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:45:43.282032 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:45:43.282053 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:45:43.282076 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:45:43.282102 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:45:43.282125 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:45:43.282146 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:45:43.282167 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:45:43.282188 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:45:43.282208 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:45:43.282235 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.36712 (* 0.0454545 = 0.153051 loss) | |
| I0404 13:45:43.282261 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.60389 (* 0.0454545 = 0.163813 loss) | |
| I0404 13:45:43.282290 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.45944 (* 0.0454545 = 0.157247 loss) | |
| I0404 13:45:43.282321 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.5602 (* 0.0454545 = 0.161827 loss) | |
| I0404 13:45:43.282349 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.38223 (* 0.0454545 = 0.153738 loss) | |
| I0404 13:45:43.282387 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.17982 (* 0.0454545 = 0.0990825 loss) | |
| I0404 13:45:43.282415 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.08667 (* 0.0454545 = 0.0493939 loss) | |
| I0404 13:45:43.282444 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.482697 (* 0.0454545 = 0.0219408 loss) | |
| I0404 13:45:43.282469 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.074513 (* 0.0454545 = 0.00338695 loss) | |
| I0404 13:45:43.282495 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0216833 (* 0.0454545 = 0.000985604 loss) | |
| I0404 13:45:43.282522 9252 solver.cpp:245] Train net output #32: loss/loss11 = 7.30228e-05 (* 0.0454545 = 3.31922e-06 loss) | |
| I0404 13:45:43.282549 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.03002e-05 (* 0.0454545 = 3.19546e-06 loss) | |
| I0404 13:45:43.282575 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.59643e-05 (* 0.0454545 = 2.99838e-06 loss) | |
| I0404 13:45:43.282603 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.53372e-05 (* 0.0454545 = 2.96987e-06 loss) | |
| I0404 13:45:43.282629 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.42831e-05 (* 0.0454545 = 3.37651e-06 loss) | |
| I0404 13:45:43.282656 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.77945e-05 (* 0.0454545 = 3.08157e-06 loss) | |
| I0404 13:45:43.282681 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.33864e-05 (* 0.0454545 = 3.33575e-06 loss) | |
| I0404 13:45:43.282730 9252 solver.cpp:245] Train net output #39: loss/loss18 = 6.94626e-05 (* 0.0454545 = 3.15739e-06 loss) | |
| I0404 13:45:43.282759 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.91403e-05 (* 0.0454545 = 3.14274e-06 loss) | |
| I0404 13:45:43.282786 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.49316e-05 (* 0.0454545 = 2.95143e-06 loss) | |
| I0404 13:45:43.282815 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.20807e-05 (* 0.0454545 = 2.82185e-06 loss) | |
| I0404 13:45:43.282845 9252 solver.cpp:245] Train net output #43: loss/loss22 = 5.97661e-05 (* 0.0454545 = 2.71664e-06 loss) | |
| I0404 13:45:43.282867 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:45:43.282891 9252 solver.cpp:245] Train net output #45: total_confidence = 2.50411e-06 | |
| I0404 13:45:43.282913 9252 sgd_solver.cpp:106] Iteration 24000, lr = 0.00976 | |
| I0404 13:46:53.500927 9252 solver.cpp:229] Iteration 24500, loss = 0.967502 | |
| I0404 13:46:53.501144 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 13:46:53.501173 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:46:53.501186 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 13:46:53.501199 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:46:53.501211 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 13:46:53.501224 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 13:46:53.501235 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 13:46:53.501246 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 1 | |
| I0404 13:46:53.501258 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 13:46:53.501271 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:46:53.501281 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:46:53.501293 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:46:53.501304 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:46:53.501317 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:46:53.501328 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:46:53.501340 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:46:53.501353 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:46:53.501363 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:46:53.501375 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:46:53.501386 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:46:53.501399 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:46:53.501410 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:46:53.501451 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.193 (* 0.0454545 = 0.145136 loss) | |
| I0404 13:46:53.501468 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.551 (* 0.0454545 = 0.161409 loss) | |
| I0404 13:46:53.501482 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.64762 (* 0.0454545 = 0.165801 loss) | |
| I0404 13:46:53.501497 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.65641 (* 0.0454545 = 0.166201 loss) | |
| I0404 13:46:53.501516 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.02264 (* 0.0454545 = 0.137393 loss) | |
| I0404 13:46:53.501530 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.32462 (* 0.0454545 = 0.105664 loss) | |
| I0404 13:46:53.501544 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.826695 (* 0.0454545 = 0.0375771 loss) | |
| I0404 13:46:53.501559 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.0913363 (* 0.0454545 = 0.00415165 loss) | |
| I0404 13:46:53.501572 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.026524 (* 0.0454545 = 0.00120564 loss) | |
| I0404 13:46:53.501586 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00965204 (* 0.0454545 = 0.000438729 loss) | |
| I0404 13:46:53.501601 9252 solver.cpp:245] Train net output #32: loss/loss11 = 7.51301e-05 (* 0.0454545 = 3.415e-06 loss) | |
| I0404 13:46:53.501616 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.48067e-05 (* 0.0454545 = 3.40031e-06 loss) | |
| I0404 13:46:53.501631 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.77399e-05 (* 0.0454545 = 3.07909e-06 loss) | |
| I0404 13:46:53.501644 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.37423e-05 (* 0.0454545 = 3.35192e-06 loss) | |
| I0404 13:46:53.501658 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.42821e-05 (* 0.0454545 = 3.37646e-06 loss) | |
| I0404 13:46:53.501672 9252 solver.cpp:245] Train net output #37: loss/loss16 = 7.48167e-05 (* 0.0454545 = 3.40076e-06 loss) | |
| I0404 13:46:53.501688 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.44914e-05 (* 0.0454545 = 3.38597e-06 loss) | |
| I0404 13:46:53.501731 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.42612e-05 (* 0.0454545 = 3.37551e-06 loss) | |
| I0404 13:46:53.501750 9252 solver.cpp:245] Train net output #40: loss/loss19 = 7.1739e-05 (* 0.0454545 = 3.26087e-06 loss) | |
| I0404 13:46:53.501765 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.51799e-05 (* 0.0454545 = 2.96273e-06 loss) | |
| I0404 13:46:53.501780 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.28305e-05 (* 0.0454545 = 2.85593e-06 loss) | |
| I0404 13:46:53.501793 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.20291e-05 (* 0.0454545 = 2.81951e-06 loss) | |
| I0404 13:46:53.501806 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:46:53.501817 9252 solver.cpp:245] Train net output #45: total_confidence = 2.14197e-05 | |
| I0404 13:46:53.501832 9252 sgd_solver.cpp:106] Iteration 24500, lr = 0.009755 | |
| I0404 13:48:04.137544 9252 solver.cpp:229] Iteration 25000, loss = 0.971615 | |
| I0404 13:48:04.137677 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 13:48:04.137697 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:48:04.137711 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 13:48:04.137722 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:48:04.137734 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 13:48:04.137748 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 13:48:04.137761 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:48:04.137773 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:48:04.137785 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:48:04.137797 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:48:04.137809 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:48:04.137820 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:48:04.137831 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:48:04.137845 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:48:04.137856 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:48:04.137867 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:48:04.137878 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:48:04.137890 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:48:04.137902 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:48:04.137913 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:48:04.137924 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:48:04.137936 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:48:04.137951 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.19211 (* 0.0454545 = 0.145096 loss) | |
| I0404 13:48:04.137972 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.45896 (* 0.0454545 = 0.157226 loss) | |
| I0404 13:48:04.137985 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.20911 (* 0.0454545 = 0.145869 loss) | |
| I0404 13:48:04.138000 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.58863 (* 0.0454545 = 0.163119 loss) | |
| I0404 13:48:04.138013 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.12091 (* 0.0454545 = 0.14186 loss) | |
| I0404 13:48:04.138027 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.30352 (* 0.0454545 = 0.104705 loss) | |
| I0404 13:48:04.138041 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.42831 (* 0.0454545 = 0.0649231 loss) | |
| I0404 13:48:04.138054 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.421165 (* 0.0454545 = 0.0191438 loss) | |
| I0404 13:48:04.138067 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.275731 (* 0.0454545 = 0.0125332 loss) | |
| I0404 13:48:04.138082 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0313853 (* 0.0454545 = 0.0014266 loss) | |
| I0404 13:48:04.138095 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.0003663 (* 0.0454545 = 1.665e-05 loss) | |
| I0404 13:48:04.138110 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000365167 (* 0.0454545 = 1.65985e-05 loss) | |
| I0404 13:48:04.138124 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000355624 (* 0.0454545 = 1.61647e-05 loss) | |
| I0404 13:48:04.138139 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000347692 (* 0.0454545 = 1.58042e-05 loss) | |
| I0404 13:48:04.138151 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000347768 (* 0.0454545 = 1.58077e-05 loss) | |
| I0404 13:48:04.138165 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000330834 (* 0.0454545 = 1.50379e-05 loss) | |
| I0404 13:48:04.138180 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000331031 (* 0.0454545 = 1.50469e-05 loss) | |
| I0404 13:48:04.138211 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000394686 (* 0.0454545 = 1.79403e-05 loss) | |
| I0404 13:48:04.138227 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000349093 (* 0.0454545 = 1.58679e-05 loss) | |
| I0404 13:48:04.138242 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000311054 (* 0.0454545 = 1.41388e-05 loss) | |
| I0404 13:48:04.138255 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000297955 (* 0.0454545 = 1.35434e-05 loss) | |
| I0404 13:48:04.138269 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000279759 (* 0.0454545 = 1.27163e-05 loss) | |
| I0404 13:48:04.138281 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:48:04.138293 9252 solver.cpp:245] Train net output #45: total_confidence = 3.12004e-05 | |
| I0404 13:48:04.138306 9252 sgd_solver.cpp:106] Iteration 25000, lr = 0.00975 | |
| I0404 13:49:14.745193 9252 solver.cpp:229] Iteration 25500, loss = 0.959719 | |
| I0404 13:49:14.745338 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 13:49:14.745357 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:49:14.745371 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 13:49:14.745383 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 13:49:14.745395 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 13:49:14.745407 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 13:49:14.745419 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:49:14.745431 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 13:49:14.745442 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:49:14.745455 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:49:14.745481 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:49:14.745493 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:49:14.745506 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:49:14.745517 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:49:14.745528 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:49:14.745540 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:49:14.745551 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:49:14.745563 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:49:14.745574 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:49:14.745585 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:49:14.745597 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:49:14.745609 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:49:14.745623 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.82556 (* 0.0454545 = 0.128435 loss) | |
| I0404 13:49:14.745638 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.16883 (* 0.0454545 = 0.144038 loss) | |
| I0404 13:49:14.745652 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.31833 (* 0.0454545 = 0.150833 loss) | |
| I0404 13:49:14.745666 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.33579 (* 0.0454545 = 0.151627 loss) | |
| I0404 13:49:14.745679 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.19431 (* 0.0454545 = 0.145196 loss) | |
| I0404 13:49:14.745693 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.30002 (* 0.0454545 = 0.104547 loss) | |
| I0404 13:49:14.745707 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.36 (* 0.0454545 = 0.0618184 loss) | |
| I0404 13:49:14.745720 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.82059 (* 0.0454545 = 0.0372996 loss) | |
| I0404 13:49:14.745735 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.402626 (* 0.0454545 = 0.0183012 loss) | |
| I0404 13:49:14.745751 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.208426 (* 0.0454545 = 0.00947391 loss) | |
| I0404 13:49:14.745766 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000136805 (* 0.0454545 = 6.21841e-06 loss) | |
| I0404 13:49:14.745781 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000116364 (* 0.0454545 = 5.28929e-06 loss) | |
| I0404 13:49:14.745795 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000126527 (* 0.0454545 = 5.75124e-06 loss) | |
| I0404 13:49:14.745808 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000128132 (* 0.0454545 = 5.8242e-06 loss) | |
| I0404 13:49:14.745823 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000134914 (* 0.0454545 = 6.13247e-06 loss) | |
| I0404 13:49:14.745836 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000152582 (* 0.0454545 = 6.93556e-06 loss) | |
| I0404 13:49:14.745851 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000132817 (* 0.0454545 = 6.03712e-06 loss) | |
| I0404 13:49:14.745882 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000137992 (* 0.0454545 = 6.27235e-06 loss) | |
| I0404 13:49:14.745898 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000130626 (* 0.0454545 = 5.93754e-06 loss) | |
| I0404 13:49:14.745911 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000116024 (* 0.0454545 = 5.27382e-06 loss) | |
| I0404 13:49:14.745925 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00012332 (* 0.0454545 = 5.60547e-06 loss) | |
| I0404 13:49:14.745939 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000114051 (* 0.0454545 = 5.18413e-06 loss) | |
| I0404 13:49:14.745950 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:49:14.745962 9252 solver.cpp:245] Train net output #45: total_confidence = 2.62668e-05 | |
| I0404 13:49:14.745976 9252 sgd_solver.cpp:106] Iteration 25500, lr = 0.009745 | |
| I0404 13:50:25.259028 9252 solver.cpp:229] Iteration 26000, loss = 0.960584 | |
| I0404 13:50:25.259151 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 13:50:25.259172 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 13:50:25.259186 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 13:50:25.259198 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:50:25.259212 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 13:50:25.259227 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 13:50:25.259240 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 13:50:25.259253 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 13:50:25.259268 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:50:25.259280 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:50:25.259294 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:50:25.259307 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:50:25.259320 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:50:25.259331 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:50:25.259344 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:50:25.259356 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:50:25.259369 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:50:25.259380 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:50:25.259392 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:50:25.259405 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:50:25.259419 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:50:25.259431 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:50:25.259449 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.06789 (* 0.0454545 = 0.13945 loss) | |
| I0404 13:50:25.259464 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.19138 (* 0.0454545 = 0.145063 loss) | |
| I0404 13:50:25.259480 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.26821 (* 0.0454545 = 0.148555 loss) | |
| I0404 13:50:25.259496 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.53588 (* 0.0454545 = 0.160722 loss) | |
| I0404 13:50:25.259510 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.09095 (* 0.0454545 = 0.140498 loss) | |
| I0404 13:50:25.259526 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.81941 (* 0.0454545 = 0.128155 loss) | |
| I0404 13:50:25.259542 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.43726 (* 0.0454545 = 0.0653299 loss) | |
| I0404 13:50:25.259557 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.318364 (* 0.0454545 = 0.0144711 loss) | |
| I0404 13:50:25.259572 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.444633 (* 0.0454545 = 0.0202106 loss) | |
| I0404 13:50:25.259588 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00576267 (* 0.0454545 = 0.00026194 loss) | |
| I0404 13:50:25.259601 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.33726e-05 (* 0.0454545 = 1.97148e-06 loss) | |
| I0404 13:50:25.259620 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.36132e-05 (* 0.0454545 = 1.98242e-06 loss) | |
| I0404 13:50:25.259636 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.24172e-05 (* 0.0454545 = 1.92805e-06 loss) | |
| I0404 13:50:25.259652 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.29348e-05 (* 0.0454545 = 1.95158e-06 loss) | |
| I0404 13:50:25.259667 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.578e-05 (* 0.0454545 = 2.08091e-06 loss) | |
| I0404 13:50:25.259681 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.07464e-05 (* 0.0454545 = 1.85211e-06 loss) | |
| I0404 13:50:25.259697 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.41851e-05 (* 0.0454545 = 2.00841e-06 loss) | |
| I0404 13:50:25.259727 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.4728e-05 (* 0.0454545 = 2.03309e-06 loss) | |
| I0404 13:50:25.259749 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.20969e-05 (* 0.0454545 = 1.91349e-06 loss) | |
| I0404 13:50:25.259766 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.94493e-05 (* 0.0454545 = 1.79315e-06 loss) | |
| I0404 13:50:25.259783 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.72788e-05 (* 0.0454545 = 1.69449e-06 loss) | |
| I0404 13:50:25.259799 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.62897e-05 (* 0.0454545 = 1.64953e-06 loss) | |
| I0404 13:50:25.259814 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:50:25.259829 9252 solver.cpp:245] Train net output #45: total_confidence = 5.48898e-05 | |
| I0404 13:50:25.259843 9252 sgd_solver.cpp:106] Iteration 26000, lr = 0.00974 | |
| I0404 13:51:35.566723 9252 solver.cpp:229] Iteration 26500, loss = 0.95366 | |
| I0404 13:51:35.566958 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 13:51:35.566979 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 13:51:35.566993 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:51:35.567005 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 13:51:35.567018 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 13:51:35.567029 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 13:51:35.567041 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:51:35.567054 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 13:51:35.567065 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:51:35.567076 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 13:51:35.567088 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:51:35.567101 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:51:35.567111 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:51:35.567123 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:51:35.567136 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:51:35.567154 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:51:35.567168 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:51:35.567190 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:51:35.567204 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:51:35.567215 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:51:35.567227 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:51:35.567239 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:51:35.567255 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.29603 (* 0.0454545 = 0.14982 loss) | |
| I0404 13:51:35.567268 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.28075 (* 0.0454545 = 0.149125 loss) | |
| I0404 13:51:35.567282 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.5913 (* 0.0454545 = 0.163241 loss) | |
| I0404 13:51:35.567302 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.29195 (* 0.0454545 = 0.149634 loss) | |
| I0404 13:51:35.567332 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.25299 (* 0.0454545 = 0.147863 loss) | |
| I0404 13:51:35.567363 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.04235 (* 0.0454545 = 0.092834 loss) | |
| I0404 13:51:35.567400 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.33999 (* 0.0454545 = 0.0609086 loss) | |
| I0404 13:51:35.567430 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.814932 (* 0.0454545 = 0.0370424 loss) | |
| I0404 13:51:35.567450 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.441443 (* 0.0454545 = 0.0200656 loss) | |
| I0404 13:51:35.567463 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.465259 (* 0.0454545 = 0.0211482 loss) | |
| I0404 13:51:35.567478 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.73017e-05 (* 0.0454545 = 1.69553e-06 loss) | |
| I0404 13:51:35.567492 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.58599e-05 (* 0.0454545 = 1.62999e-06 loss) | |
| I0404 13:51:35.567507 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.63462e-05 (* 0.0454545 = 1.6521e-06 loss) | |
| I0404 13:51:35.567520 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.48166e-05 (* 0.0454545 = 1.58257e-06 loss) | |
| I0404 13:51:35.567534 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.91032e-05 (* 0.0454545 = 1.77742e-06 loss) | |
| I0404 13:51:35.567548 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.15065e-05 (* 0.0454545 = 1.88666e-06 loss) | |
| I0404 13:51:35.567562 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.71359e-05 (* 0.0454545 = 1.688e-06 loss) | |
| I0404 13:51:35.567592 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.82835e-05 (* 0.0454545 = 1.74016e-06 loss) | |
| I0404 13:51:35.567607 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.70019e-05 (* 0.0454545 = 1.6819e-06 loss) | |
| I0404 13:51:35.567621 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.3006e-05 (* 0.0454545 = 1.50027e-06 loss) | |
| I0404 13:51:35.567636 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.47943e-05 (* 0.0454545 = 1.58156e-06 loss) | |
| I0404 13:51:35.567649 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.45951e-05 (* 0.0454545 = 1.5725e-06 loss) | |
| I0404 13:51:35.567662 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:51:35.567672 9252 solver.cpp:245] Train net output #45: total_confidence = 2.43642e-05 | |
| I0404 13:51:35.567687 9252 sgd_solver.cpp:106] Iteration 26500, lr = 0.009735 | |
| I0404 13:52:46.152940 9252 solver.cpp:229] Iteration 27000, loss = 0.95207 | |
| I0404 13:52:46.153074 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 13:52:46.153095 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 13:52:46.153108 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 13:52:46.153120 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:52:46.153132 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.375 | |
| I0404 13:52:46.153144 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.53125 | |
| I0404 13:52:46.153157 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 13:52:46.153168 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:52:46.153180 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:52:46.153192 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:52:46.153203 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:52:46.153215 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:52:46.153228 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:52:46.153239 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:52:46.153250 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:52:46.153262 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:52:46.153273 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:52:46.153285 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:52:46.153296 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:52:46.153308 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:52:46.153321 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:52:46.153331 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:52:46.153348 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.96839 (* 0.0454545 = 0.134927 loss) | |
| I0404 13:52:46.153362 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.60791 (* 0.0454545 = 0.163996 loss) | |
| I0404 13:52:46.153376 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.35949 (* 0.0454545 = 0.152704 loss) | |
| I0404 13:52:46.153390 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.44417 (* 0.0454545 = 0.156553 loss) | |
| I0404 13:52:46.153404 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.55831 (* 0.0454545 = 0.116287 loss) | |
| I0404 13:52:46.153431 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.89288 (* 0.0454545 = 0.08604 loss) | |
| I0404 13:52:46.153448 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.664262 (* 0.0454545 = 0.0301937 loss) | |
| I0404 13:52:46.153462 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.446953 (* 0.0454545 = 0.020316 loss) | |
| I0404 13:52:46.153476 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.272952 (* 0.0454545 = 0.0124069 loss) | |
| I0404 13:52:46.153491 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.244248 (* 0.0454545 = 0.0111022 loss) | |
| I0404 13:52:46.153504 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000119298 (* 0.0454545 = 5.42262e-06 loss) | |
| I0404 13:52:46.153519 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000113349 (* 0.0454545 = 5.15223e-06 loss) | |
| I0404 13:52:46.153533 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000108603 (* 0.0454545 = 4.9365e-06 loss) | |
| I0404 13:52:46.153548 9252 solver.cpp:245] Train net output #35: loss/loss14 = 9.79109e-05 (* 0.0454545 = 4.4505e-06 loss) | |
| I0404 13:52:46.153561 9252 solver.cpp:245] Train net output #36: loss/loss15 = 8.91403e-05 (* 0.0454545 = 4.05183e-06 loss) | |
| I0404 13:52:46.153574 9252 solver.cpp:245] Train net output #37: loss/loss16 = 8.51991e-05 (* 0.0454545 = 3.87269e-06 loss) | |
| I0404 13:52:46.153589 9252 solver.cpp:245] Train net output #38: loss/loss17 = 8.78856e-05 (* 0.0454545 = 3.9948e-06 loss) | |
| I0404 13:52:46.153622 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000102463 (* 0.0454545 = 4.65741e-06 loss) | |
| I0404 13:52:46.153637 9252 solver.cpp:245] Train net output #40: loss/loss19 = 9.39548e-05 (* 0.0454545 = 4.27067e-06 loss) | |
| I0404 13:52:46.153651 9252 solver.cpp:245] Train net output #41: loss/loss20 = 8.69895e-05 (* 0.0454545 = 3.95407e-06 loss) | |
| I0404 13:52:46.153666 9252 solver.cpp:245] Train net output #42: loss/loss21 = 8.58984e-05 (* 0.0454545 = 3.90447e-06 loss) | |
| I0404 13:52:46.153679 9252 solver.cpp:245] Train net output #43: loss/loss22 = 8.42832e-05 (* 0.0454545 = 3.83105e-06 loss) | |
| I0404 13:52:46.153695 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:52:46.153707 9252 solver.cpp:245] Train net output #45: total_confidence = 5.81788e-05 | |
| I0404 13:52:46.153722 9252 sgd_solver.cpp:106] Iteration 27000, lr = 0.00973 | |
| I0404 13:53:56.935788 9252 solver.cpp:229] Iteration 27500, loss = 0.949568 | |
| I0404 13:53:56.935889 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 13:53:56.935910 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 13:53:56.935925 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 13:53:56.935936 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 13:53:56.935948 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 13:53:56.935961 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 13:53:56.935972 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 13:53:56.935986 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:53:56.935997 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:53:56.936008 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:53:56.936020 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:53:56.936031 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:53:56.936043 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:53:56.936054 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:53:56.936066 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:53:56.936077 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:53:56.936089 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:53:56.936100 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:53:56.936111 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:53:56.936122 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:53:56.936134 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:53:56.936146 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:53:56.936161 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.36713 (* 0.0454545 = 0.153051 loss) | |
| I0404 13:53:56.936174 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.14371 (* 0.0454545 = 0.142896 loss) | |
| I0404 13:53:56.936188 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.16087 (* 0.0454545 = 0.143676 loss) | |
| I0404 13:53:56.936202 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.15145 (* 0.0454545 = 0.143248 loss) | |
| I0404 13:53:56.936215 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.14596 (* 0.0454545 = 0.142998 loss) | |
| I0404 13:53:56.936229 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.19021 (* 0.0454545 = 0.099555 loss) | |
| I0404 13:53:56.936242 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.25175 (* 0.0454545 = 0.0568979 loss) | |
| I0404 13:53:56.936256 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.538634 (* 0.0454545 = 0.0244834 loss) | |
| I0404 13:53:56.936270 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.244794 (* 0.0454545 = 0.011127 loss) | |
| I0404 13:53:56.936285 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0156319 (* 0.0454545 = 0.000710543 loss) | |
| I0404 13:53:56.936298 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000452435 (* 0.0454545 = 2.05652e-05 loss) | |
| I0404 13:53:56.936312 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000504826 (* 0.0454545 = 2.29466e-05 loss) | |
| I0404 13:53:56.936326 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000507103 (* 0.0454545 = 2.30501e-05 loss) | |
| I0404 13:53:56.936341 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000447672 (* 0.0454545 = 2.03487e-05 loss) | |
| I0404 13:53:56.936354 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000475295 (* 0.0454545 = 2.16043e-05 loss) | |
| I0404 13:53:56.936368 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000294177 (* 0.0454545 = 1.33717e-05 loss) | |
| I0404 13:53:56.936381 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000382349 (* 0.0454545 = 1.73795e-05 loss) | |
| I0404 13:53:56.936413 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000447049 (* 0.0454545 = 2.03204e-05 loss) | |
| I0404 13:53:56.936427 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00038492 (* 0.0454545 = 1.74964e-05 loss) | |
| I0404 13:53:56.936441 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000407547 (* 0.0454545 = 1.85249e-05 loss) | |
| I0404 13:53:56.936455 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00044738 (* 0.0454545 = 2.03355e-05 loss) | |
| I0404 13:53:56.936468 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000354452 (* 0.0454545 = 1.61115e-05 loss) | |
| I0404 13:53:56.936480 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:53:56.936492 9252 solver.cpp:245] Train net output #45: total_confidence = 4.36455e-05 | |
| I0404 13:53:56.936506 9252 sgd_solver.cpp:106] Iteration 27500, lr = 0.009725 | |
| I0404 13:55:08.183051 9252 solver.cpp:229] Iteration 28000, loss = 0.946584 | |
| I0404 13:55:08.183177 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 13:55:08.183197 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 13:55:08.183209 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:55:08.183223 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 13:55:08.183234 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 13:55:08.183246 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 13:55:08.183260 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 13:55:08.183272 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:55:08.183284 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:55:08.183295 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:55:08.183307 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:55:08.183320 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:55:08.183334 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:55:08.183346 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:55:08.183357 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:55:08.183369 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:55:08.183380 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:55:08.183393 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:55:08.183403 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:55:08.183415 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:55:08.183428 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:55:08.183439 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:55:08.183454 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.22906 (* 0.0454545 = 0.146775 loss) | |
| I0404 13:55:08.183467 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.3675 (* 0.0454545 = 0.153068 loss) | |
| I0404 13:55:08.183482 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.38021 (* 0.0454545 = 0.153646 loss) | |
| I0404 13:55:08.183496 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.64103 (* 0.0454545 = 0.165501 loss) | |
| I0404 13:55:08.183509 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.23626 (* 0.0454545 = 0.147103 loss) | |
| I0404 13:55:08.183523 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.76876 (* 0.0454545 = 0.125853 loss) | |
| I0404 13:55:08.183537 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.36486 (* 0.0454545 = 0.0620391 loss) | |
| I0404 13:55:08.183550 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.69835 (* 0.0454545 = 0.0317432 loss) | |
| I0404 13:55:08.183564 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.524981 (* 0.0454545 = 0.0238628 loss) | |
| I0404 13:55:08.183578 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.193814 (* 0.0454545 = 0.00880973 loss) | |
| I0404 13:55:08.183593 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000253202 (* 0.0454545 = 1.15092e-05 loss) | |
| I0404 13:55:08.183607 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000258583 (* 0.0454545 = 1.17538e-05 loss) | |
| I0404 13:55:08.183621 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000247678 (* 0.0454545 = 1.12581e-05 loss) | |
| I0404 13:55:08.183635 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000256744 (* 0.0454545 = 1.16702e-05 loss) | |
| I0404 13:55:08.183650 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000250296 (* 0.0454545 = 1.13771e-05 loss) | |
| I0404 13:55:08.183665 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000212463 (* 0.0454545 = 9.6574e-06 loss) | |
| I0404 13:55:08.183678 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000242553 (* 0.0454545 = 1.10251e-05 loss) | |
| I0404 13:55:08.183708 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000240793 (* 0.0454545 = 1.09451e-05 loss) | |
| I0404 13:55:08.183724 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000240119 (* 0.0454545 = 1.09145e-05 loss) | |
| I0404 13:55:08.183738 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00024157 (* 0.0454545 = 1.09805e-05 loss) | |
| I0404 13:55:08.183756 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000227022 (* 0.0454545 = 1.03192e-05 loss) | |
| I0404 13:55:08.183770 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000210853 (* 0.0454545 = 9.58422e-06 loss) | |
| I0404 13:55:08.183782 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:55:08.183794 9252 solver.cpp:245] Train net output #45: total_confidence = 9.50214e-06 | |
| I0404 13:55:08.183809 9252 sgd_solver.cpp:106] Iteration 28000, lr = 0.00972 | |
| I0404 13:56:19.297998 9252 solver.cpp:229] Iteration 28500, loss = 0.947349 | |
| I0404 13:56:19.298166 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.15625 | |
| I0404 13:56:19.298194 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 13:56:19.298208 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 13:56:19.298220 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0404 13:56:19.298233 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.09375 | |
| I0404 13:56:19.298244 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.3125 | |
| I0404 13:56:19.298261 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.65625 | |
| I0404 13:56:19.298274 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 13:56:19.298285 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 13:56:19.298296 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 13:56:19.298308 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:56:19.298319 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:56:19.298332 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:56:19.298344 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:56:19.298355 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:56:19.298367 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:56:19.298378 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:56:19.298389 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:56:19.298401 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:56:19.298413 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:56:19.298424 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:56:19.298435 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:56:19.298451 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.95208 (* 0.0454545 = 0.134185 loss) | |
| I0404 13:56:19.298475 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.25016 (* 0.0454545 = 0.147734 loss) | |
| I0404 13:56:19.298488 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.43281 (* 0.0454545 = 0.156037 loss) | |
| I0404 13:56:19.298501 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.36991 (* 0.0454545 = 0.153178 loss) | |
| I0404 13:56:19.298516 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.37012 (* 0.0454545 = 0.153187 loss) | |
| I0404 13:56:19.298529 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.87147 (* 0.0454545 = 0.130521 loss) | |
| I0404 13:56:19.298550 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.83471 (* 0.0454545 = 0.0833961 loss) | |
| I0404 13:56:19.298564 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.676122 (* 0.0454545 = 0.0307328 loss) | |
| I0404 13:56:19.298578 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.416573 (* 0.0454545 = 0.0189351 loss) | |
| I0404 13:56:19.298591 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.442655 (* 0.0454545 = 0.0201207 loss) | |
| I0404 13:56:19.298605 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.85304e-05 (* 0.0454545 = 1.29683e-06 loss) | |
| I0404 13:56:19.298619 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.78746e-05 (* 0.0454545 = 1.26703e-06 loss) | |
| I0404 13:56:19.298634 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.87242e-05 (* 0.0454545 = 1.30565e-06 loss) | |
| I0404 13:56:19.298647 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.61571e-05 (* 0.0454545 = 1.18896e-06 loss) | |
| I0404 13:56:19.298661 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.95365e-05 (* 0.0454545 = 1.34257e-06 loss) | |
| I0404 13:56:19.298674 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.01737e-05 (* 0.0454545 = 1.37153e-06 loss) | |
| I0404 13:56:19.298688 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.77406e-05 (* 0.0454545 = 1.26094e-06 loss) | |
| I0404 13:56:19.298717 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.01289e-05 (* 0.0454545 = 1.3695e-06 loss) | |
| I0404 13:56:19.298732 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.96222e-05 (* 0.0454545 = 1.34646e-06 loss) | |
| I0404 13:56:19.298748 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.52294e-05 (* 0.0454545 = 1.14679e-06 loss) | |
| I0404 13:56:19.298766 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.55871e-05 (* 0.0454545 = 1.16305e-06 loss) | |
| I0404 13:56:19.298796 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.60118e-05 (* 0.0454545 = 1.18236e-06 loss) | |
| I0404 13:56:19.298810 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:56:19.298821 9252 solver.cpp:245] Train net output #45: total_confidence = 3.70985e-05 | |
| I0404 13:56:19.298836 9252 sgd_solver.cpp:106] Iteration 28500, lr = 0.009715 | |
| I0404 13:57:30.481798 9252 solver.cpp:229] Iteration 29000, loss = 0.945074 | |
| I0404 13:57:30.481940 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 13:57:30.481959 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 13:57:30.481973 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 13:57:30.481986 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 13:57:30.481998 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 13:57:30.482010 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 13:57:30.482023 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 13:57:30.482033 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 13:57:30.482045 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:57:30.482058 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 13:57:30.482069 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:57:30.482080 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:57:30.482092 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:57:30.482103 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:57:30.482122 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:57:30.482134 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:57:30.482146 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:57:30.482157 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:57:30.482168 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:57:30.482180 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:57:30.482192 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:57:30.482203 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:57:30.482228 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.22017 (* 0.0454545 = 0.146371 loss) | |
| I0404 13:57:30.482241 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.56801 (* 0.0454545 = 0.162182 loss) | |
| I0404 13:57:30.482255 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.50457 (* 0.0454545 = 0.159299 loss) | |
| I0404 13:57:30.482270 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.67049 (* 0.0454545 = 0.166841 loss) | |
| I0404 13:57:30.482283 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.13531 (* 0.0454545 = 0.142514 loss) | |
| I0404 13:57:30.482297 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.14632 (* 0.0454545 = 0.0975599 loss) | |
| I0404 13:57:30.482311 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.967904 (* 0.0454545 = 0.0439956 loss) | |
| I0404 13:57:30.482326 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.153097 (* 0.0454545 = 0.00695897 loss) | |
| I0404 13:57:30.482341 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.175029 (* 0.0454545 = 0.00795588 loss) | |
| I0404 13:57:30.482354 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00558973 (* 0.0454545 = 0.000254079 loss) | |
| I0404 13:57:30.482369 9252 solver.cpp:245] Train net output #32: loss/loss11 = 5.16588e-05 (* 0.0454545 = 2.34813e-06 loss) | |
| I0404 13:57:30.482383 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.03027e-05 (* 0.0454545 = 2.28649e-06 loss) | |
| I0404 13:57:30.482398 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.98783e-05 (* 0.0454545 = 2.2672e-06 loss) | |
| I0404 13:57:30.482411 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.8544e-05 (* 0.0454545 = 2.20655e-06 loss) | |
| I0404 13:57:30.482426 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.21693e-05 (* 0.0454545 = 2.37133e-06 loss) | |
| I0404 13:57:30.482440 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.9952e-05 (* 0.0454545 = 2.27054e-06 loss) | |
| I0404 13:57:30.482455 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.28435e-05 (* 0.0454545 = 2.40198e-06 loss) | |
| I0404 13:57:30.482487 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.81956e-05 (* 0.0454545 = 2.19071e-06 loss) | |
| I0404 13:57:30.482502 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.98962e-05 (* 0.0454545 = 2.26801e-06 loss) | |
| I0404 13:57:30.482517 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.59783e-05 (* 0.0454545 = 2.08992e-06 loss) | |
| I0404 13:57:30.482530 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.85831e-05 (* 0.0454545 = 2.20832e-06 loss) | |
| I0404 13:57:30.482544 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.86835e-05 (* 0.0454545 = 2.21288e-06 loss) | |
| I0404 13:57:30.482556 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:57:30.482568 9252 solver.cpp:245] Train net output #45: total_confidence = 4.40952e-05 | |
| I0404 13:57:30.482583 9252 sgd_solver.cpp:106] Iteration 29000, lr = 0.00971 | |
| I0404 13:58:41.218505 9252 solver.cpp:229] Iteration 29500, loss = 0.940375 | |
| I0404 13:58:41.218611 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.15625 | |
| I0404 13:58:41.218629 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 13:58:41.218642 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 13:58:41.218654 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 13:58:41.218667 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 13:58:41.218679 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 13:58:41.218694 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 13:58:41.218706 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 13:58:41.218719 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 13:58:41.218730 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 13:58:41.218750 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 13:58:41.218761 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 13:58:41.218773 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 13:58:41.218785 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 13:58:41.218796 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 13:58:41.218808 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 13:58:41.218821 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 13:58:41.218832 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 13:58:41.218843 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 13:58:41.218864 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 13:58:41.218875 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 13:58:41.218888 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 13:58:41.218905 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.06288 (* 0.0454545 = 0.139222 loss) | |
| I0404 13:58:41.218920 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.27225 (* 0.0454545 = 0.148739 loss) | |
| I0404 13:58:41.218935 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.23537 (* 0.0454545 = 0.147062 loss) | |
| I0404 13:58:41.218950 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.38006 (* 0.0454545 = 0.153639 loss) | |
| I0404 13:58:41.218963 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.91483 (* 0.0454545 = 0.132492 loss) | |
| I0404 13:58:41.218976 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.63511 (* 0.0454545 = 0.119778 loss) | |
| I0404 13:58:41.218991 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.07584 (* 0.0454545 = 0.0489018 loss) | |
| I0404 13:58:41.219005 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.571437 (* 0.0454545 = 0.0259744 loss) | |
| I0404 13:58:41.219020 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.325218 (* 0.0454545 = 0.0147826 loss) | |
| I0404 13:58:41.219033 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.328369 (* 0.0454545 = 0.0149259 loss) | |
| I0404 13:58:41.219048 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.05569e-05 (* 0.0454545 = 9.34406e-07 loss) | |
| I0404 13:58:41.219063 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.06576e-05 (* 0.0454545 = 9.38981e-07 loss) | |
| I0404 13:58:41.219076 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.84408e-05 (* 0.0454545 = 8.38219e-07 loss) | |
| I0404 13:58:41.219090 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.00987e-05 (* 0.0454545 = 9.13579e-07 loss) | |
| I0404 13:58:41.219111 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.08401e-05 (* 0.0454545 = 9.47278e-07 loss) | |
| I0404 13:58:41.219125 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.20566e-05 (* 0.0454545 = 1.00257e-06 loss) | |
| I0404 13:58:41.219140 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.22633e-05 (* 0.0454545 = 1.01197e-06 loss) | |
| I0404 13:58:41.219169 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.75653e-05 (* 0.0454545 = 7.98423e-07 loss) | |
| I0404 13:58:41.219185 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.08029e-05 (* 0.0454545 = 9.45585e-07 loss) | |
| I0404 13:58:41.219199 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.89661e-05 (* 0.0454545 = 8.62096e-07 loss) | |
| I0404 13:58:41.219213 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.81093e-05 (* 0.0454545 = 8.23149e-07 loss) | |
| I0404 13:58:41.219235 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.87464e-05 (* 0.0454545 = 8.52107e-07 loss) | |
| I0404 13:58:41.219247 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 13:58:41.219259 9252 solver.cpp:245] Train net output #45: total_confidence = 3.47666e-05 | |
| I0404 13:58:41.219272 9252 sgd_solver.cpp:106] Iteration 29500, lr = 0.009705 | |
| I0404 13:59:52.146591 9252 solver.cpp:338] Iteration 30000, Testing net (#0) | |
| I0404 14:00:00.308490 9252 solver.cpp:393] Test loss: 0.861114 | |
| I0404 14:00:00.308540 9252 solver.cpp:406] Test net output #0: loss/accuracy01 = 0.067 | |
| I0404 14:00:00.308557 9252 solver.cpp:406] Test net output #1: loss/accuracy02 = 0.105 | |
| I0404 14:00:00.308569 9252 solver.cpp:406] Test net output #2: loss/accuracy03 = 0.093 | |
| I0404 14:00:00.308581 9252 solver.cpp:406] Test net output #3: loss/accuracy04 = 0.115 | |
| I0404 14:00:00.308593 9252 solver.cpp:406] Test net output #4: loss/accuracy05 = 0.213 | |
| I0404 14:00:00.308604 9252 solver.cpp:406] Test net output #5: loss/accuracy06 = 0.5 | |
| I0404 14:00:00.308616 9252 solver.cpp:406] Test net output #6: loss/accuracy07 = 0.893 | |
| I0404 14:00:00.308627 9252 solver.cpp:406] Test net output #7: loss/accuracy08 = 0.97 | |
| I0404 14:00:00.308640 9252 solver.cpp:406] Test net output #8: loss/accuracy09 = 0.995 | |
| I0404 14:00:00.308650 9252 solver.cpp:406] Test net output #9: loss/accuracy10 = 0.998 | |
| I0404 14:00:00.308661 9252 solver.cpp:406] Test net output #10: loss/accuracy11 = 1 | |
| I0404 14:00:00.308673 9252 solver.cpp:406] Test net output #11: loss/accuracy12 = 1 | |
| I0404 14:00:00.308684 9252 solver.cpp:406] Test net output #12: loss/accuracy13 = 1 | |
| I0404 14:00:00.308696 9252 solver.cpp:406] Test net output #13: loss/accuracy14 = 1 | |
| I0404 14:00:00.308706 9252 solver.cpp:406] Test net output #14: loss/accuracy15 = 1 | |
| I0404 14:00:00.308717 9252 solver.cpp:406] Test net output #15: loss/accuracy16 = 1 | |
| I0404 14:00:00.308728 9252 solver.cpp:406] Test net output #16: loss/accuracy17 = 1 | |
| I0404 14:00:00.308740 9252 solver.cpp:406] Test net output #17: loss/accuracy18 = 1 | |
| I0404 14:00:00.308754 9252 solver.cpp:406] Test net output #18: loss/accuracy19 = 1 | |
| I0404 14:00:00.308765 9252 solver.cpp:406] Test net output #19: loss/accuracy20 = 1 | |
| I0404 14:00:00.308776 9252 solver.cpp:406] Test net output #20: loss/accuracy21 = 1 | |
| I0404 14:00:00.308787 9252 solver.cpp:406] Test net output #21: loss/accuracy22 = 1 | |
| I0404 14:00:00.308802 9252 solver.cpp:406] Test net output #22: loss/loss01 = 3.24853 (* 0.0454545 = 0.147661 loss) | |
| I0404 14:00:00.308817 9252 solver.cpp:406] Test net output #23: loss/loss02 = 3.06678 (* 0.0454545 = 0.139399 loss) | |
| I0404 14:00:00.308830 9252 solver.cpp:406] Test net output #24: loss/loss03 = 3.2398 (* 0.0454545 = 0.147264 loss) | |
| I0404 14:00:00.308845 9252 solver.cpp:406] Test net output #25: loss/loss04 = 3.2237 (* 0.0454545 = 0.146532 loss) | |
| I0404 14:00:00.308857 9252 solver.cpp:406] Test net output #26: loss/loss05 = 3.16447 (* 0.0454545 = 0.14384 loss) | |
| I0404 14:00:00.308871 9252 solver.cpp:406] Test net output #27: loss/loss06 = 2.03204 (* 0.0454545 = 0.0923653 loss) | |
| I0404 14:00:00.308884 9252 solver.cpp:406] Test net output #28: loss/loss07 = 0.645133 (* 0.0454545 = 0.0293242 loss) | |
| I0404 14:00:00.308897 9252 solver.cpp:406] Test net output #29: loss/loss08 = 0.2453 (* 0.0454545 = 0.01115 loss) | |
| I0404 14:00:00.308912 9252 solver.cpp:406] Test net output #30: loss/loss09 = 0.0510983 (* 0.0454545 = 0.00232265 loss) | |
| I0404 14:00:00.308925 9252 solver.cpp:406] Test net output #31: loss/loss10 = 0.0243444 (* 0.0454545 = 0.00110656 loss) | |
| I0404 14:00:00.308939 9252 solver.cpp:406] Test net output #32: loss/loss11 = 0.000269771 (* 0.0454545 = 1.22623e-05 loss) | |
| I0404 14:00:00.308953 9252 solver.cpp:406] Test net output #33: loss/loss12 = 0.00028896 (* 0.0454545 = 1.31345e-05 loss) | |
| I0404 14:00:00.308966 9252 solver.cpp:406] Test net output #34: loss/loss13 = 0.000276089 (* 0.0454545 = 1.25495e-05 loss) | |
| I0404 14:00:00.308980 9252 solver.cpp:406] Test net output #35: loss/loss14 = 0.000267652 (* 0.0454545 = 1.2166e-05 loss) | |
| I0404 14:00:00.308993 9252 solver.cpp:406] Test net output #36: loss/loss15 = 0.000295689 (* 0.0454545 = 1.34404e-05 loss) | |
| I0404 14:00:00.309007 9252 solver.cpp:406] Test net output #37: loss/loss16 = 0.000286034 (* 0.0454545 = 1.30016e-05 loss) | |
| I0404 14:00:00.309020 9252 solver.cpp:406] Test net output #38: loss/loss17 = 0.000280882 (* 0.0454545 = 1.27674e-05 loss) | |
| I0404 14:00:00.309067 9252 solver.cpp:406] Test net output #39: loss/loss18 = 0.000258109 (* 0.0454545 = 1.17322e-05 loss) | |
| I0404 14:00:00.309083 9252 solver.cpp:406] Test net output #40: loss/loss19 = 0.000285402 (* 0.0454545 = 1.29728e-05 loss) | |
| I0404 14:00:00.309097 9252 solver.cpp:406] Test net output #41: loss/loss20 = 0.000267373 (* 0.0454545 = 1.21533e-05 loss) | |
| I0404 14:00:00.309110 9252 solver.cpp:406] Test net output #42: loss/loss21 = 0.000277652 (* 0.0454545 = 1.26205e-05 loss) | |
| I0404 14:00:00.309123 9252 solver.cpp:406] Test net output #43: loss/loss22 = 0.000267483 (* 0.0454545 = 1.21583e-05 loss) | |
| I0404 14:00:00.309135 9252 solver.cpp:406] Test net output #44: total_accuracy = 0 | |
| I0404 14:00:00.309146 9252 solver.cpp:406] Test net output #45: total_confidence = 2.86206e-05 | |
| I0404 14:00:00.342829 9252 solver.cpp:229] Iteration 30000, loss = 0.936281 | |
| I0404 14:00:00.342867 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 14:00:00.342883 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:00:00.342895 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:00:00.342911 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 14:00:00.342922 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 14:00:00.342934 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 14:00:00.342947 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 14:00:00.342958 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 1 | |
| I0404 14:00:00.342970 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 14:00:00.342981 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:00:00.342993 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:00:00.343004 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:00:00.343016 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:00:00.343027 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:00:00.343039 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:00:00.343050 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:00:00.343061 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:00:00.343072 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:00:00.343085 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:00:00.343096 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:00:00.343106 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:00:00.343118 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:00:00.343132 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.05402 (* 0.0454545 = 0.138819 loss) | |
| I0404 14:00:00.343147 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.13417 (* 0.0454545 = 0.142462 loss) | |
| I0404 14:00:00.343160 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.23265 (* 0.0454545 = 0.146939 loss) | |
| I0404 14:00:00.343174 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.19718 (* 0.0454545 = 0.145326 loss) | |
| I0404 14:00:00.343188 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.99243 (* 0.0454545 = 0.136019 loss) | |
| I0404 14:00:00.343200 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.47976 (* 0.0454545 = 0.112717 loss) | |
| I0404 14:00:00.343214 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.822114 (* 0.0454545 = 0.0373688 loss) | |
| I0404 14:00:00.343227 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.0700228 (* 0.0454545 = 0.00318285 loss) | |
| I0404 14:00:00.343241 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.00925662 (* 0.0454545 = 0.000420755 loss) | |
| I0404 14:00:00.343255 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00341973 (* 0.0454545 = 0.000155442 loss) | |
| I0404 14:00:00.343287 9252 solver.cpp:245] Train net output #32: loss/loss11 = 8.36845e-05 (* 0.0454545 = 3.80384e-06 loss) | |
| I0404 14:00:00.343302 9252 solver.cpp:245] Train net output #33: loss/loss12 = 9.53658e-05 (* 0.0454545 = 4.33481e-06 loss) | |
| I0404 14:00:00.343317 9252 solver.cpp:245] Train net output #34: loss/loss13 = 9.51639e-05 (* 0.0454545 = 4.32563e-06 loss) | |
| I0404 14:00:00.343330 9252 solver.cpp:245] Train net output #35: loss/loss14 = 8.39161e-05 (* 0.0454545 = 3.81437e-06 loss) | |
| I0404 14:00:00.343343 9252 solver.cpp:245] Train net output #36: loss/loss15 = 9.73535e-05 (* 0.0454545 = 4.42516e-06 loss) | |
| I0404 14:00:00.343358 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.76425e-05 (* 0.0454545 = 3.07466e-06 loss) | |
| I0404 14:00:00.343370 9252 solver.cpp:245] Train net output #38: loss/loss17 = 8.23992e-05 (* 0.0454545 = 3.74542e-06 loss) | |
| I0404 14:00:00.343384 9252 solver.cpp:245] Train net output #39: loss/loss18 = 8.29397e-05 (* 0.0454545 = 3.76999e-06 loss) | |
| I0404 14:00:00.343399 9252 solver.cpp:245] Train net output #40: loss/loss19 = 7.53423e-05 (* 0.0454545 = 3.42465e-06 loss) | |
| I0404 14:00:00.343411 9252 solver.cpp:245] Train net output #41: loss/loss20 = 8.51966e-05 (* 0.0454545 = 3.87257e-06 loss) | |
| I0404 14:00:00.343425 9252 solver.cpp:245] Train net output #42: loss/loss21 = 8.68959e-05 (* 0.0454545 = 3.94982e-06 loss) | |
| I0404 14:00:00.343438 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.92196e-05 (* 0.0454545 = 3.60089e-06 loss) | |
| I0404 14:00:00.343451 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:00:00.343466 9252 solver.cpp:245] Train net output #45: total_confidence = 4.99387e-05 | |
| I0404 14:00:00.343482 9252 sgd_solver.cpp:106] Iteration 30000, lr = 0.0097 | |
| I0404 14:01:11.341557 9252 solver.cpp:229] Iteration 30500, loss = 0.937498 | |
| I0404 14:01:11.341774 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.15625 | |
| I0404 14:01:11.341794 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:01:11.341807 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.1875 | |
| I0404 14:01:11.341820 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 14:01:11.341832 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 14:01:11.341845 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 14:01:11.341856 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 14:01:11.341867 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:01:11.341879 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:01:11.341892 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:01:11.341905 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:01:11.341917 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:01:11.341929 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:01:11.341940 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:01:11.341953 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:01:11.341964 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:01:11.341975 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:01:11.341987 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:01:11.341998 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:01:11.342010 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:01:11.342021 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:01:11.342032 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:01:11.342048 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.9398 (* 0.0454545 = 0.133627 loss) | |
| I0404 14:01:11.342062 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.09795 (* 0.0454545 = 0.140816 loss) | |
| I0404 14:01:11.342077 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.99885 (* 0.0454545 = 0.136312 loss) | |
| I0404 14:01:11.342092 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.23834 (* 0.0454545 = 0.147197 loss) | |
| I0404 14:01:11.342106 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.24782 (* 0.0454545 = 0.147628 loss) | |
| I0404 14:01:11.342120 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.22384 (* 0.0454545 = 0.101084 loss) | |
| I0404 14:01:11.342133 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.07652 (* 0.0454545 = 0.0489327 loss) | |
| I0404 14:01:11.342147 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.524983 (* 0.0454545 = 0.0238629 loss) | |
| I0404 14:01:11.342161 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.245824 (* 0.0454545 = 0.0111738 loss) | |
| I0404 14:01:11.342175 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0419386 (* 0.0454545 = 0.0019063 loss) | |
| I0404 14:01:11.342190 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000328696 (* 0.0454545 = 1.49407e-05 loss) | |
| I0404 14:01:11.342205 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000339163 (* 0.0454545 = 1.54165e-05 loss) | |
| I0404 14:01:11.342218 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000329349 (* 0.0454545 = 1.49704e-05 loss) | |
| I0404 14:01:11.342232 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000325839 (* 0.0454545 = 1.48109e-05 loss) | |
| I0404 14:01:11.342245 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000331606 (* 0.0454545 = 1.5073e-05 loss) | |
| I0404 14:01:11.342259 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.00030078 (* 0.0454545 = 1.36718e-05 loss) | |
| I0404 14:01:11.342273 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000315306 (* 0.0454545 = 1.43321e-05 loss) | |
| I0404 14:01:11.342305 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000338982 (* 0.0454545 = 1.54083e-05 loss) | |
| I0404 14:01:11.342320 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00030698 (* 0.0454545 = 1.39536e-05 loss) | |
| I0404 14:01:11.342334 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000322318 (* 0.0454545 = 1.46508e-05 loss) | |
| I0404 14:01:11.342349 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000311272 (* 0.0454545 = 1.41487e-05 loss) | |
| I0404 14:01:11.342361 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000300339 (* 0.0454545 = 1.36518e-05 loss) | |
| I0404 14:01:11.342373 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:01:11.342386 9252 solver.cpp:245] Train net output #45: total_confidence = 4.15045e-05 | |
| I0404 14:01:11.342399 9252 sgd_solver.cpp:106] Iteration 30500, lr = 0.009695 | |
| I0404 14:02:22.482519 9252 solver.cpp:229] Iteration 31000, loss = 0.936463 | |
| I0404 14:02:22.482681 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 14:02:22.482702 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 14:02:22.482715 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 14:02:22.482728 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 14:02:22.482739 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 14:02:22.482753 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 14:02:22.482766 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 14:02:22.482779 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:02:22.482790 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:02:22.482803 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:02:22.482815 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:02:22.482826 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:02:22.482838 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:02:22.482849 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:02:22.482861 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:02:22.482872 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:02:22.482883 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:02:22.482895 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:02:22.482906 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:02:22.482918 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:02:22.482929 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:02:22.482941 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:02:22.482956 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.98089 (* 0.0454545 = 0.135495 loss) | |
| I0404 14:02:22.482971 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.93642 (* 0.0454545 = 0.133473 loss) | |
| I0404 14:02:22.482985 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.15237 (* 0.0454545 = 0.143289 loss) | |
| I0404 14:02:22.483000 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.21167 (* 0.0454545 = 0.145985 loss) | |
| I0404 14:02:22.483013 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.60301 (* 0.0454545 = 0.118318 loss) | |
| I0404 14:02:22.483027 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.17639 (* 0.0454545 = 0.0989269 loss) | |
| I0404 14:02:22.483042 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.3573 (* 0.0454545 = 0.0616955 loss) | |
| I0404 14:02:22.483055 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.690159 (* 0.0454545 = 0.0313709 loss) | |
| I0404 14:02:22.483069 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.132206 (* 0.0454545 = 0.00600937 loss) | |
| I0404 14:02:22.483083 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.11446 (* 0.0454545 = 0.00520275 loss) | |
| I0404 14:02:22.483098 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000135282 (* 0.0454545 = 6.14917e-06 loss) | |
| I0404 14:02:22.483113 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000107907 (* 0.0454545 = 4.90488e-06 loss) | |
| I0404 14:02:22.483126 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000130905 (* 0.0454545 = 5.95021e-06 loss) | |
| I0404 14:02:22.483140 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000134035 (* 0.0454545 = 6.09251e-06 loss) | |
| I0404 14:02:22.483155 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000108365 (* 0.0454545 = 4.92567e-06 loss) | |
| I0404 14:02:22.483168 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000141278 (* 0.0454545 = 6.42172e-06 loss) | |
| I0404 14:02:22.483182 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000110187 (* 0.0454545 = 5.0085e-06 loss) | |
| I0404 14:02:22.483214 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000124064 (* 0.0454545 = 5.63926e-06 loss) | |
| I0404 14:02:22.483230 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000120714 (* 0.0454545 = 5.48699e-06 loss) | |
| I0404 14:02:22.483244 9252 solver.cpp:245] Train net output #41: loss/loss20 = 9.89084e-05 (* 0.0454545 = 4.49584e-06 loss) | |
| I0404 14:02:22.483258 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000126551 (* 0.0454545 = 5.75233e-06 loss) | |
| I0404 14:02:22.483273 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000112625 (* 0.0454545 = 5.1193e-06 loss) | |
| I0404 14:02:22.483284 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:02:22.483295 9252 solver.cpp:245] Train net output #45: total_confidence = 2.77447e-05 | |
| I0404 14:02:22.483310 9252 sgd_solver.cpp:106] Iteration 31000, lr = 0.00969 | |
| I0404 14:03:33.116291 9252 solver.cpp:229] Iteration 31500, loss = 0.92887 | |
| I0404 14:03:33.116443 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.09375 | |
| I0404 14:03:33.116468 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:03:33.116488 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 14:03:33.116513 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 14:03:33.116525 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 14:03:33.116538 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 14:03:33.116549 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.875 | |
| I0404 14:03:33.116561 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 14:03:33.116574 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 14:03:33.116585 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:03:33.116596 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:03:33.116608 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:03:33.116621 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:03:33.116631 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:03:33.116643 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:03:33.116654 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:03:33.116667 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:03:33.116677 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:03:33.116689 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:03:33.116701 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:03:33.116714 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:03:33.116724 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:03:33.116739 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.12677 (* 0.0454545 = 0.142126 loss) | |
| I0404 14:03:33.116755 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.17357 (* 0.0454545 = 0.144253 loss) | |
| I0404 14:03:33.116767 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.23753 (* 0.0454545 = 0.147161 loss) | |
| I0404 14:03:33.116781 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.36072 (* 0.0454545 = 0.15276 loss) | |
| I0404 14:03:33.116796 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.03301 (* 0.0454545 = 0.137864 loss) | |
| I0404 14:03:33.116809 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.0081 (* 0.0454545 = 0.0912773 loss) | |
| I0404 14:03:33.116823 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.685438 (* 0.0454545 = 0.0311563 loss) | |
| I0404 14:03:33.116837 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.173597 (* 0.0454545 = 0.00789079 loss) | |
| I0404 14:03:33.116852 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.010956 (* 0.0454545 = 0.000497999 loss) | |
| I0404 14:03:33.116865 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00447457 (* 0.0454545 = 0.000203389 loss) | |
| I0404 14:03:33.116880 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.63162e-05 (* 0.0454545 = 1.19619e-06 loss) | |
| I0404 14:03:33.116894 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.44274e-05 (* 0.0454545 = 1.11034e-06 loss) | |
| I0404 14:03:33.116922 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.271e-05 (* 0.0454545 = 1.03227e-06 loss) | |
| I0404 14:03:33.116955 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.45541e-05 (* 0.0454545 = 1.11609e-06 loss) | |
| I0404 14:03:33.116986 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.64131e-05 (* 0.0454545 = 1.20059e-06 loss) | |
| I0404 14:03:33.117015 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.55562e-05 (* 0.0454545 = 1.16165e-06 loss) | |
| I0404 14:03:33.117038 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.56419e-05 (* 0.0454545 = 1.16554e-06 loss) | |
| I0404 14:03:33.117069 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.29745e-05 (* 0.0454545 = 1.04429e-06 loss) | |
| I0404 14:03:33.117085 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.53774e-05 (* 0.0454545 = 1.15352e-06 loss) | |
| I0404 14:03:33.117100 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.28478e-05 (* 0.0454545 = 1.03854e-06 loss) | |
| I0404 14:03:33.117113 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.32874e-05 (* 0.0454545 = 1.05852e-06 loss) | |
| I0404 14:03:33.117127 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.33545e-05 (* 0.0454545 = 1.06157e-06 loss) | |
| I0404 14:03:33.117139 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:03:33.117151 9252 solver.cpp:245] Train net output #45: total_confidence = 2.92121e-05 | |
| I0404 14:03:33.117164 9252 sgd_solver.cpp:106] Iteration 31500, lr = 0.009685 | |
| I0404 14:04:44.919107 9252 solver.cpp:229] Iteration 32000, loss = 0.933316 | |
| I0404 14:04:44.919221 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 14:04:44.919240 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 14:04:44.919253 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 14:04:44.919265 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 14:04:44.919277 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 14:04:44.919289 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 14:04:44.919301 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 14:04:44.919313 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:04:44.919324 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:04:44.919337 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:04:44.919348 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:04:44.919359 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:04:44.919370 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:04:44.919383 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:04:44.919394 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:04:44.919404 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:04:44.919416 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:04:44.919427 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:04:44.919438 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:04:44.919450 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:04:44.919461 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:04:44.919472 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:04:44.919488 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.79985 (* 0.0454545 = 0.127266 loss) | |
| I0404 14:04:44.919502 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.06166 (* 0.0454545 = 0.139166 loss) | |
| I0404 14:04:44.919517 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.01437 (* 0.0454545 = 0.137017 loss) | |
| I0404 14:04:44.919529 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.15536 (* 0.0454545 = 0.143425 loss) | |
| I0404 14:04:44.919543 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.90941 (* 0.0454545 = 0.132246 loss) | |
| I0404 14:04:44.919556 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.21213 (* 0.0454545 = 0.100551 loss) | |
| I0404 14:04:44.919570 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.898313 (* 0.0454545 = 0.0408324 loss) | |
| I0404 14:04:44.919584 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.374252 (* 0.0454545 = 0.0170114 loss) | |
| I0404 14:04:44.919598 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.198828 (* 0.0454545 = 0.00903764 loss) | |
| I0404 14:04:44.919612 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00515792 (* 0.0454545 = 0.000234451 loss) | |
| I0404 14:04:44.919628 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.62447e-05 (* 0.0454545 = 1.19294e-06 loss) | |
| I0404 14:04:44.919642 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.63789e-05 (* 0.0454545 = 1.19904e-06 loss) | |
| I0404 14:04:44.919656 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.62637e-05 (* 0.0454545 = 1.1938e-06 loss) | |
| I0404 14:04:44.919670 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.5086e-05 (* 0.0454545 = 1.14027e-06 loss) | |
| I0404 14:04:44.919683 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.81786e-05 (* 0.0454545 = 1.28085e-06 loss) | |
| I0404 14:04:44.919697 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.4622e-05 (* 0.0454545 = 1.11918e-06 loss) | |
| I0404 14:04:44.919711 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.53802e-05 (* 0.0454545 = 1.15365e-06 loss) | |
| I0404 14:04:44.919742 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.61852e-05 (* 0.0454545 = 1.19024e-06 loss) | |
| I0404 14:04:44.919760 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.53486e-05 (* 0.0454545 = 1.15221e-06 loss) | |
| I0404 14:04:44.919775 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.63698e-05 (* 0.0454545 = 1.19863e-06 loss) | |
| I0404 14:04:44.919788 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.62971e-05 (* 0.0454545 = 1.19532e-06 loss) | |
| I0404 14:04:44.919802 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.54885e-05 (* 0.0454545 = 1.15857e-06 loss) | |
| I0404 14:04:44.919814 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:04:44.919826 9252 solver.cpp:245] Train net output #45: total_confidence = 3.29479e-05 | |
| I0404 14:04:44.919838 9252 sgd_solver.cpp:106] Iteration 32000, lr = 0.00968 | |
| I0404 14:05:55.706950 9252 solver.cpp:229] Iteration 32500, loss = 0.932208 | |
| I0404 14:05:55.707159 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.15625 | |
| I0404 14:05:55.707177 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:05:55.707191 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:05:55.707203 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 14:05:55.707216 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 14:05:55.707227 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 14:05:55.707239 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 14:05:55.707250 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.75 | |
| I0404 14:05:55.707262 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.875 | |
| I0404 14:05:55.707273 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.90625 | |
| I0404 14:05:55.707285 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:05:55.707296 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:05:55.707309 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:05:55.707319 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:05:55.707331 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:05:55.707342 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:05:55.707355 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:05:55.707365 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:05:55.707376 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:05:55.707388 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:05:55.707399 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:05:55.707412 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:05:55.707427 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.01952 (* 0.0454545 = 0.137251 loss) | |
| I0404 14:05:55.707440 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.37508 (* 0.0454545 = 0.153413 loss) | |
| I0404 14:05:55.707454 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.14564 (* 0.0454545 = 0.142983 loss) | |
| I0404 14:05:55.707468 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.23795 (* 0.0454545 = 0.14718 loss) | |
| I0404 14:05:55.707485 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.9976 (* 0.0454545 = 0.136255 loss) | |
| I0404 14:05:55.707499 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.19952 (* 0.0454545 = 0.0999782 loss) | |
| I0404 14:05:55.707514 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.51359 (* 0.0454545 = 0.0687996 loss) | |
| I0404 14:05:55.707526 9252 solver.cpp:245] Train net output #29: loss/loss08 = 1.06772 (* 0.0454545 = 0.0485328 loss) | |
| I0404 14:05:55.707540 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.59726 (* 0.0454545 = 0.0271482 loss) | |
| I0404 14:05:55.707553 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.535808 (* 0.0454545 = 0.0243549 loss) | |
| I0404 14:05:55.707568 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.00154675 (* 0.0454545 = 7.03066e-05 loss) | |
| I0404 14:05:55.707582 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.00175185 (* 0.0454545 = 7.96294e-05 loss) | |
| I0404 14:05:55.707597 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.00181554 (* 0.0454545 = 8.25244e-05 loss) | |
| I0404 14:05:55.707612 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.00157473 (* 0.0454545 = 7.15785e-05 loss) | |
| I0404 14:05:55.707624 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.00162769 (* 0.0454545 = 7.39857e-05 loss) | |
| I0404 14:05:55.707638 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.00134524 (* 0.0454545 = 6.11472e-05 loss) | |
| I0404 14:05:55.707653 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.00143117 (* 0.0454545 = 6.50533e-05 loss) | |
| I0404 14:05:55.707684 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.00148601 (* 0.0454545 = 6.7546e-05 loss) | |
| I0404 14:05:55.707700 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.00151822 (* 0.0454545 = 6.90102e-05 loss) | |
| I0404 14:05:55.707713 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00155327 (* 0.0454545 = 7.06033e-05 loss) | |
| I0404 14:05:55.707727 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00150658 (* 0.0454545 = 6.8481e-05 loss) | |
| I0404 14:05:55.707741 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.00145754 (* 0.0454545 = 6.62517e-05 loss) | |
| I0404 14:05:55.707752 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:05:55.707764 9252 solver.cpp:245] Train net output #45: total_confidence = 5.34663e-05 | |
| I0404 14:05:55.707777 9252 sgd_solver.cpp:106] Iteration 32500, lr = 0.009675 | |
| I0404 14:07:06.788790 9252 solver.cpp:229] Iteration 33000, loss = 0.931617 | |
| I0404 14:07:06.788929 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 14:07:06.788949 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 14:07:06.788961 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 14:07:06.788974 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 14:07:06.788986 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 14:07:06.788998 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 14:07:06.789010 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 14:07:06.789021 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:07:06.789033 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:07:06.789046 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:07:06.789057 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:07:06.789068 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:07:06.789079 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:07:06.789091 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:07:06.789104 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:07:06.789116 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:07:06.789127 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:07:06.789139 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:07:06.789150 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:07:06.789161 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:07:06.789173 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:07:06.789185 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:07:06.789201 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.0732 (* 0.0454545 = 0.139691 loss) | |
| I0404 14:07:06.789216 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.30847 (* 0.0454545 = 0.150385 loss) | |
| I0404 14:07:06.789229 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.31443 (* 0.0454545 = 0.150656 loss) | |
| I0404 14:07:06.789243 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.40776 (* 0.0454545 = 0.154898 loss) | |
| I0404 14:07:06.789258 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.09744 (* 0.0454545 = 0.140793 loss) | |
| I0404 14:07:06.789271 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.96599 (* 0.0454545 = 0.089363 loss) | |
| I0404 14:07:06.789285 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.787353 (* 0.0454545 = 0.0357888 loss) | |
| I0404 14:07:06.789299 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.237384 (* 0.0454545 = 0.0107902 loss) | |
| I0404 14:07:06.789314 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.169899 (* 0.0454545 = 0.0077227 loss) | |
| I0404 14:07:06.789327 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0119675 (* 0.0454545 = 0.000543979 loss) | |
| I0404 14:07:06.789342 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.70407e-05 (* 0.0454545 = 2.13821e-06 loss) | |
| I0404 14:07:06.789356 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.21559e-05 (* 0.0454545 = 2.37072e-06 loss) | |
| I0404 14:07:06.789371 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.5827e-05 (* 0.0454545 = 2.08305e-06 loss) | |
| I0404 14:07:06.789384 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.37093e-05 (* 0.0454545 = 1.98679e-06 loss) | |
| I0404 14:07:06.789398 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.87601e-05 (* 0.0454545 = 2.21637e-06 loss) | |
| I0404 14:07:06.789412 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.0908e-05 (* 0.0454545 = 1.85945e-06 loss) | |
| I0404 14:07:06.789441 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.59593e-05 (* 0.0454545 = 2.08906e-06 loss) | |
| I0404 14:07:06.789475 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.7332e-05 (* 0.0454545 = 2.15145e-06 loss) | |
| I0404 14:07:06.789490 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.25064e-05 (* 0.0454545 = 1.93211e-06 loss) | |
| I0404 14:07:06.789505 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.47585e-05 (* 0.0454545 = 2.03448e-06 loss) | |
| I0404 14:07:06.789518 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.49371e-05 (* 0.0454545 = 2.04259e-06 loss) | |
| I0404 14:07:06.789532 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.41262e-05 (* 0.0454545 = 2.00573e-06 loss) | |
| I0404 14:07:06.789544 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:07:06.789556 9252 solver.cpp:245] Train net output #45: total_confidence = 6.55089e-05 | |
| I0404 14:07:06.789569 9252 sgd_solver.cpp:106] Iteration 33000, lr = 0.00967 | |
| I0404 14:08:17.652562 9252 solver.cpp:229] Iteration 33500, loss = 0.93067 | |
| I0404 14:08:17.652772 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.21875 | |
| I0404 14:08:17.652793 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:08:17.652806 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:08:17.652819 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 14:08:17.652832 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.0625 | |
| I0404 14:08:17.652843 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 14:08:17.652854 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.53125 | |
| I0404 14:08:17.652866 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.71875 | |
| I0404 14:08:17.652878 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 14:08:17.652890 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 14:08:17.652902 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:08:17.652915 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:08:17.652926 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:08:17.652937 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:08:17.652950 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:08:17.652961 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:08:17.652972 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:08:17.652984 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:08:17.652997 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:08:17.653007 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:08:17.653025 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:08:17.653043 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:08:17.653060 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.93528 (* 0.0454545 = 0.133422 loss) | |
| I0404 14:08:17.653075 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.0508 (* 0.0454545 = 0.138673 loss) | |
| I0404 14:08:17.653090 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.18069 (* 0.0454545 = 0.144577 loss) | |
| I0404 14:08:17.653106 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.26757 (* 0.0454545 = 0.148526 loss) | |
| I0404 14:08:17.653120 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.11886 (* 0.0454545 = 0.141767 loss) | |
| I0404 14:08:17.653134 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.85929 (* 0.0454545 = 0.129968 loss) | |
| I0404 14:08:17.653147 9252 solver.cpp:245] Train net output #28: loss/loss07 = 2.09266 (* 0.0454545 = 0.095121 loss) | |
| I0404 14:08:17.653162 9252 solver.cpp:245] Train net output #29: loss/loss08 = 1.21449 (* 0.0454545 = 0.0552041 loss) | |
| I0404 14:08:17.653175 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.440637 (* 0.0454545 = 0.0200289 loss) | |
| I0404 14:08:17.653189 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.275854 (* 0.0454545 = 0.0125388 loss) | |
| I0404 14:08:17.653203 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.00022146 (* 0.0454545 = 1.00664e-05 loss) | |
| I0404 14:08:17.653218 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000216302 (* 0.0454545 = 9.83192e-06 loss) | |
| I0404 14:08:17.653233 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000219561 (* 0.0454545 = 9.98005e-06 loss) | |
| I0404 14:08:17.653246 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000206621 (* 0.0454545 = 9.39188e-06 loss) | |
| I0404 14:08:17.653260 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000203776 (* 0.0454545 = 9.26255e-06 loss) | |
| I0404 14:08:17.653275 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.00019919 (* 0.0454545 = 9.05409e-06 loss) | |
| I0404 14:08:17.653288 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000193412 (* 0.0454545 = 8.79145e-06 loss) | |
| I0404 14:08:17.653317 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000197743 (* 0.0454545 = 8.98831e-06 loss) | |
| I0404 14:08:17.653332 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000191649 (* 0.0454545 = 8.71132e-06 loss) | |
| I0404 14:08:17.653347 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000184116 (* 0.0454545 = 8.36889e-06 loss) | |
| I0404 14:08:17.653360 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000197168 (* 0.0454545 = 8.96217e-06 loss) | |
| I0404 14:08:17.653374 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000185646 (* 0.0454545 = 8.43847e-06 loss) | |
| I0404 14:08:17.653386 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:08:17.653398 9252 solver.cpp:245] Train net output #45: total_confidence = 4.84199e-06 | |
| I0404 14:08:17.653412 9252 sgd_solver.cpp:106] Iteration 33500, lr = 0.009665 | |
| I0404 14:09:29.037900 9252 solver.cpp:229] Iteration 34000, loss = 0.92165 | |
| I0404 14:09:29.038029 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.15625 | |
| I0404 14:09:29.038048 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:09:29.038063 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 14:09:29.038074 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 14:09:29.038086 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.09375 | |
| I0404 14:09:29.038099 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.28125 | |
| I0404 14:09:29.038111 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 14:09:29.038123 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:09:29.038136 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:09:29.038156 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:09:29.038168 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:09:29.038180 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:09:29.038192 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:09:29.038203 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:09:29.038218 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:09:29.038229 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:09:29.038241 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:09:29.038254 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:09:29.038264 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:09:29.038276 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:09:29.038288 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:09:29.038300 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:09:29.038316 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.66134 (* 0.0454545 = 0.12097 loss) | |
| I0404 14:09:29.038329 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.04893 (* 0.0454545 = 0.138588 loss) | |
| I0404 14:09:29.038344 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.14006 (* 0.0454545 = 0.14273 loss) | |
| I0404 14:09:29.038358 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.0679 (* 0.0454545 = 0.13945 loss) | |
| I0404 14:09:29.038372 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.21068 (* 0.0454545 = 0.14594 loss) | |
| I0404 14:09:29.038386 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.60329 (* 0.0454545 = 0.118331 loss) | |
| I0404 14:09:29.038400 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.73718 (* 0.0454545 = 0.0789626 loss) | |
| I0404 14:09:29.038414 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.666611 (* 0.0454545 = 0.0303005 loss) | |
| I0404 14:09:29.038427 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.159284 (* 0.0454545 = 0.00724017 loss) | |
| I0404 14:09:29.038441 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.143511 (* 0.0454545 = 0.00652324 loss) | |
| I0404 14:09:29.038456 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.65761e-05 (* 0.0454545 = 3.02619e-06 loss) | |
| I0404 14:09:29.038470 9252 solver.cpp:245] Train net output #33: loss/loss12 = 6.19374e-05 (* 0.0454545 = 2.81534e-06 loss) | |
| I0404 14:09:29.038501 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.55133e-05 (* 0.0454545 = 2.97788e-06 loss) | |
| I0404 14:09:29.038516 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.44749e-05 (* 0.0454545 = 2.93068e-06 loss) | |
| I0404 14:09:29.038537 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.60848e-05 (* 0.0454545 = 2.54931e-06 loss) | |
| I0404 14:09:29.038558 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.43205e-05 (* 0.0454545 = 2.92366e-06 loss) | |
| I0404 14:09:29.038573 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.69988e-05 (* 0.0454545 = 2.59085e-06 loss) | |
| I0404 14:09:29.038606 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.73717e-05 (* 0.0454545 = 2.60781e-06 loss) | |
| I0404 14:09:29.038621 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.86196e-05 (* 0.0454545 = 2.66453e-06 loss) | |
| I0404 14:09:29.038636 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.21803e-05 (* 0.0454545 = 2.37183e-06 loss) | |
| I0404 14:09:29.038650 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.61836e-05 (* 0.0454545 = 2.5538e-06 loss) | |
| I0404 14:09:29.038663 9252 solver.cpp:245] Train net output #43: loss/loss22 = 5.58737e-05 (* 0.0454545 = 2.53972e-06 loss) | |
| I0404 14:09:29.038676 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:09:29.038687 9252 solver.cpp:245] Train net output #45: total_confidence = 2.65879e-05 | |
| I0404 14:09:29.038700 9252 sgd_solver.cpp:106] Iteration 34000, lr = 0.00966 | |
| I0404 14:10:39.985522 9252 solver.cpp:229] Iteration 34500, loss = 0.924682 | |
| I0404 14:10:39.985635 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 14:10:39.985652 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 14:10:39.985673 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 14:10:39.985685 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.25 | |
| I0404 14:10:39.985697 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 14:10:39.985709 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 14:10:39.985720 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 14:10:39.985734 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:10:39.985746 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 14:10:39.985759 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:10:39.985769 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:10:39.985781 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:10:39.985793 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:10:39.985805 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:10:39.985816 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:10:39.985827 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:10:39.985839 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:10:39.985851 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:10:39.985862 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:10:39.985873 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:10:39.985884 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:10:39.985895 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:10:39.985914 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.86047 (* 0.0454545 = 0.130022 loss) | |
| I0404 14:10:39.985929 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.05769 (* 0.0454545 = 0.138986 loss) | |
| I0404 14:10:39.985942 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.07526 (* 0.0454545 = 0.139784 loss) | |
| I0404 14:10:39.985955 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.17558 (* 0.0454545 = 0.144345 loss) | |
| I0404 14:10:39.985970 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.82158 (* 0.0454545 = 0.128254 loss) | |
| I0404 14:10:39.985983 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.91652 (* 0.0454545 = 0.0871144 loss) | |
| I0404 14:10:39.985996 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.38512 (* 0.0454545 = 0.0629598 loss) | |
| I0404 14:10:39.986009 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.60342 (* 0.0454545 = 0.0274282 loss) | |
| I0404 14:10:39.986023 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.476163 (* 0.0454545 = 0.0216438 loss) | |
| I0404 14:10:39.986037 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.118011 (* 0.0454545 = 0.00536414 loss) | |
| I0404 14:10:39.986052 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000172653 (* 0.0454545 = 7.84785e-06 loss) | |
| I0404 14:10:39.986066 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000191274 (* 0.0454545 = 8.69428e-06 loss) | |
| I0404 14:10:39.986080 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000176913 (* 0.0454545 = 8.0415e-06 loss) | |
| I0404 14:10:39.986094 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.00017163 (* 0.0454545 = 7.80137e-06 loss) | |
| I0404 14:10:39.986109 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000168109 (* 0.0454545 = 7.64133e-06 loss) | |
| I0404 14:10:39.986122 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000171664 (* 0.0454545 = 7.80293e-06 loss) | |
| I0404 14:10:39.986135 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000165684 (* 0.0454545 = 7.53111e-06 loss) | |
| I0404 14:10:39.986166 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000174789 (* 0.0454545 = 7.94496e-06 loss) | |
| I0404 14:10:39.986181 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000165625 (* 0.0454545 = 7.52839e-06 loss) | |
| I0404 14:10:39.986196 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000161936 (* 0.0454545 = 7.36073e-06 loss) | |
| I0404 14:10:39.986209 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000147794 (* 0.0454545 = 6.71792e-06 loss) | |
| I0404 14:10:39.986223 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000167914 (* 0.0454545 = 7.63243e-06 loss) | |
| I0404 14:10:39.986235 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:10:39.986246 9252 solver.cpp:245] Train net output #45: total_confidence = 7.76038e-05 | |
| I0404 14:10:39.986260 9252 sgd_solver.cpp:106] Iteration 34500, lr = 0.009655 | |
| I0404 14:11:50.831578 9252 solver.cpp:229] Iteration 35000, loss = 0.91845 | |
| I0404 14:11:50.831696 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.0625 | |
| I0404 14:11:50.831714 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 14:11:50.831727 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:11:50.831743 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 14:11:50.831755 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.125 | |
| I0404 14:11:50.831768 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.3125 | |
| I0404 14:11:50.831780 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 14:11:50.831792 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:11:50.831804 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:11:50.831815 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:11:50.831827 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:11:50.831840 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:11:50.831851 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:11:50.831862 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:11:50.831873 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:11:50.831892 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:11:50.831905 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:11:50.831918 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:11:50.831929 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:11:50.831951 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:11:50.831969 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:11:50.831980 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:11:50.831996 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.43859 (* 0.0454545 = 0.1563 loss) | |
| I0404 14:11:50.832010 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.52372 (* 0.0454545 = 0.160169 loss) | |
| I0404 14:11:50.832026 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.31351 (* 0.0454545 = 0.150614 loss) | |
| I0404 14:11:50.832047 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.53269 (* 0.0454545 = 0.160577 loss) | |
| I0404 14:11:50.832062 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.29516 (* 0.0454545 = 0.14978 loss) | |
| I0404 14:11:50.832074 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.86188 (* 0.0454545 = 0.130085 loss) | |
| I0404 14:11:50.832088 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.950274 (* 0.0454545 = 0.0431943 loss) | |
| I0404 14:11:50.832103 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.468775 (* 0.0454545 = 0.021308 loss) | |
| I0404 14:11:50.832116 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.27398 (* 0.0454545 = 0.0124536 loss) | |
| I0404 14:11:50.832130 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.264887 (* 0.0454545 = 0.0120403 loss) | |
| I0404 14:11:50.832144 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.3964e-05 (* 0.0454545 = 1.99836e-06 loss) | |
| I0404 14:11:50.832159 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.29231e-05 (* 0.0454545 = 1.95105e-06 loss) | |
| I0404 14:11:50.832173 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.13641e-05 (* 0.0454545 = 1.88019e-06 loss) | |
| I0404 14:11:50.832187 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.05531e-05 (* 0.0454545 = 1.84332e-06 loss) | |
| I0404 14:11:50.832201 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.53439e-05 (* 0.0454545 = 2.06109e-06 loss) | |
| I0404 14:11:50.832216 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.19942e-05 (* 0.0454545 = 1.90883e-06 loss) | |
| I0404 14:11:50.832228 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.22262e-05 (* 0.0454545 = 1.91937e-06 loss) | |
| I0404 14:11:50.832260 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.17642e-05 (* 0.0454545 = 1.89837e-06 loss) | |
| I0404 14:11:50.832276 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.31005e-05 (* 0.0454545 = 1.95911e-06 loss) | |
| I0404 14:11:50.832290 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.68436e-05 (* 0.0454545 = 1.67471e-06 loss) | |
| I0404 14:11:50.832304 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.70969e-05 (* 0.0454545 = 1.68622e-06 loss) | |
| I0404 14:11:50.832317 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.00506e-05 (* 0.0454545 = 1.82048e-06 loss) | |
| I0404 14:11:50.832329 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:11:50.832341 9252 solver.cpp:245] Train net output #45: total_confidence = 3.48146e-05 | |
| I0404 14:11:50.832355 9252 sgd_solver.cpp:106] Iteration 35000, lr = 0.00965 | |
| I0404 14:13:02.142143 9252 solver.cpp:229] Iteration 35500, loss = 0.921058 | |
| I0404 14:13:02.142308 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.21875 | |
| I0404 14:13:02.142328 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 14:13:02.142343 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 14:13:02.142364 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 14:13:02.142375 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 14:13:02.142387 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 14:13:02.142400 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 14:13:02.142410 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 14:13:02.142422 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:13:02.142434 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:13:02.142446 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:13:02.142458 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:13:02.142470 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:13:02.142482 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:13:02.142493 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:13:02.142504 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:13:02.142516 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:13:02.142527 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:13:02.142539 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:13:02.142550 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:13:02.142562 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:13:02.142573 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:13:02.142598 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.80459 (* 0.0454545 = 0.127481 loss) | |
| I0404 14:13:02.142621 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.15388 (* 0.0454545 = 0.143358 loss) | |
| I0404 14:13:02.142637 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.24167 (* 0.0454545 = 0.147349 loss) | |
| I0404 14:13:02.142650 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.2069 (* 0.0454545 = 0.145768 loss) | |
| I0404 14:13:02.142673 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.71386 (* 0.0454545 = 0.123357 loss) | |
| I0404 14:13:02.142688 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.90398 (* 0.0454545 = 0.0865448 loss) | |
| I0404 14:13:02.142700 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.2338 (* 0.0454545 = 0.0560818 loss) | |
| I0404 14:13:02.142714 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.77381 (* 0.0454545 = 0.0351732 loss) | |
| I0404 14:13:02.142729 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.19947 (* 0.0454545 = 0.00906684 loss) | |
| I0404 14:13:02.142743 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0260491 (* 0.0454545 = 0.00118405 loss) | |
| I0404 14:13:02.142760 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.60338e-05 (* 0.0454545 = 2.09245e-06 loss) | |
| I0404 14:13:02.142776 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.02501e-05 (* 0.0454545 = 2.2841e-06 loss) | |
| I0404 14:13:02.142789 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.35244e-05 (* 0.0454545 = 1.97838e-06 loss) | |
| I0404 14:13:02.142803 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.69878e-05 (* 0.0454545 = 2.13581e-06 loss) | |
| I0404 14:13:02.142822 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.23951e-05 (* 0.0454545 = 1.92705e-06 loss) | |
| I0404 14:13:02.142838 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.3704e-05 (* 0.0454545 = 2.44109e-06 loss) | |
| I0404 14:13:02.142853 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.47892e-05 (* 0.0454545 = 2.03587e-06 loss) | |
| I0404 14:13:02.142881 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.97219e-05 (* 0.0454545 = 1.80554e-06 loss) | |
| I0404 14:13:02.142896 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.77572e-05 (* 0.0454545 = 2.17078e-06 loss) | |
| I0404 14:13:02.142910 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.70225e-05 (* 0.0454545 = 1.68284e-06 loss) | |
| I0404 14:13:02.142925 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.92692e-05 (* 0.0454545 = 1.78497e-06 loss) | |
| I0404 14:13:02.142938 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.13725e-05 (* 0.0454545 = 1.88057e-06 loss) | |
| I0404 14:13:02.142951 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:13:02.142962 9252 solver.cpp:245] Train net output #45: total_confidence = 5.18166e-05 | |
| I0404 14:13:02.142976 9252 sgd_solver.cpp:106] Iteration 35500, lr = 0.009645 | |
| I0404 14:14:13.261236 9252 solver.cpp:229] Iteration 36000, loss = 0.914201 | |
| I0404 14:14:13.261368 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.21875 | |
| I0404 14:14:13.261387 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:14:13.261402 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 14:14:13.261415 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 14:14:13.261427 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.375 | |
| I0404 14:14:13.261438 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 14:14:13.261451 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 14:14:13.261462 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 14:14:13.261474 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 14:14:13.261500 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:14:13.261515 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:14:13.261528 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:14:13.261539 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:14:13.261550 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:14:13.261561 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:14:13.261574 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:14:13.261585 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:14:13.261596 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:14:13.261608 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:14:13.261620 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:14:13.261631 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:14:13.261642 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:14:13.261658 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.02891 (* 0.0454545 = 0.137678 loss) | |
| I0404 14:14:13.261672 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.09687 (* 0.0454545 = 0.140767 loss) | |
| I0404 14:14:13.261687 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.22481 (* 0.0454545 = 0.146582 loss) | |
| I0404 14:14:13.261700 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.29089 (* 0.0454545 = 0.149586 loss) | |
| I0404 14:14:13.261714 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.55721 (* 0.0454545 = 0.116237 loss) | |
| I0404 14:14:13.261728 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.8282 (* 0.0454545 = 0.0831 loss) | |
| I0404 14:14:13.261741 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.35538 (* 0.0454545 = 0.0616081 loss) | |
| I0404 14:14:13.261759 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.644967 (* 0.0454545 = 0.0293167 loss) | |
| I0404 14:14:13.261772 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.045923 (* 0.0454545 = 0.00208741 loss) | |
| I0404 14:14:13.261786 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.011006 (* 0.0454545 = 0.000500274 loss) | |
| I0404 14:14:13.261801 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.00139e-05 (* 0.0454545 = 2.72791e-06 loss) | |
| I0404 14:14:13.261821 9252 solver.cpp:245] Train net output #33: loss/loss12 = 6.27716e-05 (* 0.0454545 = 2.85325e-06 loss) | |
| I0404 14:14:13.261849 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.77293e-05 (* 0.0454545 = 2.62406e-06 loss) | |
| I0404 14:14:13.261864 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.99863e-05 (* 0.0454545 = 2.72665e-06 loss) | |
| I0404 14:14:13.261878 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.66314e-05 (* 0.0454545 = 2.57416e-06 loss) | |
| I0404 14:14:13.261893 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.82212e-05 (* 0.0454545 = 2.19187e-06 loss) | |
| I0404 14:14:13.261907 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.08544e-05 (* 0.0454545 = 2.31156e-06 loss) | |
| I0404 14:14:13.261940 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.41176e-05 (* 0.0454545 = 2.45989e-06 loss) | |
| I0404 14:14:13.261955 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.1806e-05 (* 0.0454545 = 2.35482e-06 loss) | |
| I0404 14:14:13.261970 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.08382e-05 (* 0.0454545 = 2.31083e-06 loss) | |
| I0404 14:14:13.261983 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.47439e-05 (* 0.0454545 = 2.48836e-06 loss) | |
| I0404 14:14:13.261996 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.92784e-05 (* 0.0454545 = 2.23993e-06 loss) | |
| I0404 14:14:13.262008 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:14:13.262020 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000129603 | |
| I0404 14:14:13.262033 9252 sgd_solver.cpp:106] Iteration 36000, lr = 0.00964 | |
| I0404 14:15:24.494508 9252 solver.cpp:229] Iteration 36500, loss = 0.913107 | |
| I0404 14:15:24.494637 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 14:15:24.494657 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 14:15:24.494669 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:15:24.494683 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 14:15:24.494694 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 14:15:24.494706 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 14:15:24.494719 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 14:15:24.494729 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 14:15:24.494741 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:15:24.494755 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:15:24.494767 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:15:24.494781 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:15:24.494791 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:15:24.494803 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:15:24.494814 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:15:24.494827 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:15:24.494837 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:15:24.494848 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:15:24.494860 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:15:24.494879 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:15:24.494889 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:15:24.494901 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:15:24.494916 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.40437 (* 0.0454545 = 0.154744 loss) | |
| I0404 14:15:24.494930 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.15503 (* 0.0454545 = 0.143411 loss) | |
| I0404 14:15:24.494945 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.15382 (* 0.0454545 = 0.143355 loss) | |
| I0404 14:15:24.494961 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.55618 (* 0.0454545 = 0.161644 loss) | |
| I0404 14:15:24.494976 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.07067 (* 0.0454545 = 0.139576 loss) | |
| I0404 14:15:24.494989 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.60754 (* 0.0454545 = 0.118525 loss) | |
| I0404 14:15:24.495003 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.37301 (* 0.0454545 = 0.0624094 loss) | |
| I0404 14:15:24.495018 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.585972 (* 0.0454545 = 0.0266351 loss) | |
| I0404 14:15:24.495031 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.408281 (* 0.0454545 = 0.0185582 loss) | |
| I0404 14:15:24.495045 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.133216 (* 0.0454545 = 0.00605527 loss) | |
| I0404 14:15:24.495060 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000176448 (* 0.0454545 = 8.02038e-06 loss) | |
| I0404 14:15:24.495074 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000196536 (* 0.0454545 = 8.93344e-06 loss) | |
| I0404 14:15:24.495088 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000163468 (* 0.0454545 = 7.43037e-06 loss) | |
| I0404 14:15:24.495102 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000192295 (* 0.0454545 = 8.74067e-06 loss) | |
| I0404 14:15:24.495116 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000179877 (* 0.0454545 = 8.17622e-06 loss) | |
| I0404 14:15:24.495131 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.00017605 (* 0.0454545 = 8.00227e-06 loss) | |
| I0404 14:15:24.495143 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000175969 (* 0.0454545 = 7.99858e-06 loss) | |
| I0404 14:15:24.495174 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000151658 (* 0.0454545 = 6.89354e-06 loss) | |
| I0404 14:15:24.495198 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000177666 (* 0.0454545 = 8.07573e-06 loss) | |
| I0404 14:15:24.495213 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00016023 (* 0.0454545 = 7.28318e-06 loss) | |
| I0404 14:15:24.495226 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000152889 (* 0.0454545 = 6.94952e-06 loss) | |
| I0404 14:15:24.495240 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000168088 (* 0.0454545 = 7.64034e-06 loss) | |
| I0404 14:15:24.495251 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:15:24.495263 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000256091 | |
| I0404 14:15:24.495276 9252 sgd_solver.cpp:106] Iteration 36500, lr = 0.009635 | |
| I0404 14:16:35.366107 9252 solver.cpp:229] Iteration 37000, loss = 0.911055 | |
| I0404 14:16:35.366271 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.21875 | |
| I0404 14:16:35.366289 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.21875 | |
| I0404 14:16:35.366302 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 14:16:35.366314 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 14:16:35.366328 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 14:16:35.366339 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 14:16:35.366351 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 14:16:35.366363 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 14:16:35.366374 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:16:35.366386 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 14:16:35.366399 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:16:35.366410 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:16:35.366421 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:16:35.366432 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:16:35.366444 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:16:35.366456 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:16:35.366468 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:16:35.366479 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:16:35.366490 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:16:35.366503 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:16:35.366514 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:16:35.366525 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:16:35.366540 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.91428 (* 0.0454545 = 0.132467 loss) | |
| I0404 14:16:35.366554 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.00775 (* 0.0454545 = 0.136716 loss) | |
| I0404 14:16:35.366571 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.28484 (* 0.0454545 = 0.149311 loss) | |
| I0404 14:16:35.366586 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.18359 (* 0.0454545 = 0.144709 loss) | |
| I0404 14:16:35.366600 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.93416 (* 0.0454545 = 0.133371 loss) | |
| I0404 14:16:35.366613 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.30361 (* 0.0454545 = 0.10471 loss) | |
| I0404 14:16:35.366626 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.872902 (* 0.0454545 = 0.0396773 loss) | |
| I0404 14:16:35.366641 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.4153 (* 0.0454545 = 0.0188773 loss) | |
| I0404 14:16:35.366653 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.240093 (* 0.0454545 = 0.0109133 loss) | |
| I0404 14:16:35.366667 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.269795 (* 0.0454545 = 0.0122634 loss) | |
| I0404 14:16:35.366682 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.76387e-05 (* 0.0454545 = 2.1654e-06 loss) | |
| I0404 14:16:35.366696 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.92907e-05 (* 0.0454545 = 1.78594e-06 loss) | |
| I0404 14:16:35.366710 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.5288e-05 (* 0.0454545 = 2.05855e-06 loss) | |
| I0404 14:16:35.366724 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.65008e-05 (* 0.0454545 = 2.11367e-06 loss) | |
| I0404 14:16:35.366739 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.19024e-05 (* 0.0454545 = 1.90465e-06 loss) | |
| I0404 14:16:35.366752 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.31293e-05 (* 0.0454545 = 2.41497e-06 loss) | |
| I0404 14:16:35.366766 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.63846e-05 (* 0.0454545 = 2.10839e-06 loss) | |
| I0404 14:16:35.366796 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.50454e-05 (* 0.0454545 = 2.04752e-06 loss) | |
| I0404 14:16:35.366811 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.36313e-05 (* 0.0454545 = 1.98324e-06 loss) | |
| I0404 14:16:35.366824 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.10641e-05 (* 0.0454545 = 1.86655e-06 loss) | |
| I0404 14:16:35.366838 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.41068e-05 (* 0.0454545 = 2.00485e-06 loss) | |
| I0404 14:16:35.366852 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.46113e-05 (* 0.0454545 = 2.02779e-06 loss) | |
| I0404 14:16:35.366863 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:16:35.366875 9252 solver.cpp:245] Train net output #45: total_confidence = 5.93796e-05 | |
| I0404 14:16:35.366888 9252 sgd_solver.cpp:106] Iteration 37000, lr = 0.00963 | |
| I0404 14:17:46.284997 9252 solver.cpp:229] Iteration 37500, loss = 0.907985 | |
| I0404 14:17:46.285157 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 14:17:46.285177 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 14:17:46.285190 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 14:17:46.285203 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 14:17:46.285223 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 14:17:46.285234 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 14:17:46.285246 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 14:17:46.285259 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 14:17:46.285270 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 14:17:46.285281 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 14:17:46.285293 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:17:46.285305 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:17:46.285316 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:17:46.285328 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:17:46.285341 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:17:46.285351 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:17:46.285363 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:17:46.285375 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:17:46.285387 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:17:46.285419 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:17:46.285432 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:17:46.285444 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:17:46.285467 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.82458 (* 0.0454545 = 0.12839 loss) | |
| I0404 14:17:46.285483 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.14677 (* 0.0454545 = 0.143035 loss) | |
| I0404 14:17:46.285497 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.26901 (* 0.0454545 = 0.148592 loss) | |
| I0404 14:17:46.285511 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.29207 (* 0.0454545 = 0.149639 loss) | |
| I0404 14:17:46.285524 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.5598 (* 0.0454545 = 0.116354 loss) | |
| I0404 14:17:46.285538 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.13259 (* 0.0454545 = 0.096936 loss) | |
| I0404 14:17:46.285552 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.41099 (* 0.0454545 = 0.0641357 loss) | |
| I0404 14:17:46.285567 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.691673 (* 0.0454545 = 0.0314397 loss) | |
| I0404 14:17:46.285580 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.39184 (* 0.0454545 = 0.0178109 loss) | |
| I0404 14:17:46.285593 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.299213 (* 0.0454545 = 0.0136006 loss) | |
| I0404 14:17:46.285612 9252 solver.cpp:245] Train net output #32: loss/loss11 = 9.87203e-05 (* 0.0454545 = 4.48729e-06 loss) | |
| I0404 14:17:46.285627 9252 solver.cpp:245] Train net output #33: loss/loss12 = 9.20422e-05 (* 0.0454545 = 4.18373e-06 loss) | |
| I0404 14:17:46.285641 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000102966 (* 0.0454545 = 4.68025e-06 loss) | |
| I0404 14:17:46.285657 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000102734 (* 0.0454545 = 4.66972e-06 loss) | |
| I0404 14:17:46.285675 9252 solver.cpp:245] Train net output #36: loss/loss15 = 9.10921e-05 (* 0.0454545 = 4.14055e-06 loss) | |
| I0404 14:17:46.285689 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000108118 (* 0.0454545 = 4.91446e-06 loss) | |
| I0404 14:17:46.285702 9252 solver.cpp:245] Train net output #38: loss/loss17 = 9.15609e-05 (* 0.0454545 = 4.16186e-06 loss) | |
| I0404 14:17:46.285733 9252 solver.cpp:245] Train net output #39: loss/loss18 = 9.41764e-05 (* 0.0454545 = 4.28074e-06 loss) | |
| I0404 14:17:46.285748 9252 solver.cpp:245] Train net output #40: loss/loss19 = 9.37727e-05 (* 0.0454545 = 4.2624e-06 loss) | |
| I0404 14:17:46.285763 9252 solver.cpp:245] Train net output #41: loss/loss20 = 8.98563e-05 (* 0.0454545 = 4.08438e-06 loss) | |
| I0404 14:17:46.285776 9252 solver.cpp:245] Train net output #42: loss/loss21 = 9.65915e-05 (* 0.0454545 = 4.39052e-06 loss) | |
| I0404 14:17:46.285790 9252 solver.cpp:245] Train net output #43: loss/loss22 = 9.59123e-05 (* 0.0454545 = 4.35965e-06 loss) | |
| I0404 14:17:46.285804 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:17:46.285815 9252 solver.cpp:245] Train net output #45: total_confidence = 7.48365e-05 | |
| I0404 14:17:46.285830 9252 sgd_solver.cpp:106] Iteration 37500, lr = 0.009625 | |
| I0404 14:18:57.161366 9252 solver.cpp:229] Iteration 38000, loss = 0.904821 | |
| I0404 14:18:57.161509 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 14:18:57.161528 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 14:18:57.161541 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:18:57.161553 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 14:18:57.161566 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 14:18:57.161577 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 14:18:57.161589 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 14:18:57.161602 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.8125 | |
| I0404 14:18:57.161613 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:18:57.161625 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:18:57.161636 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:18:57.161648 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:18:57.161660 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:18:57.161671 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:18:57.161682 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:18:57.161695 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:18:57.161705 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:18:57.161717 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:18:57.161741 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:18:57.161756 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:18:57.161772 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:18:57.161792 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:18:57.161810 9252 solver.cpp:245] Train net output #22: loss/loss01 = 3.17614 (* 0.0454545 = 0.14437 loss) | |
| I0404 14:18:57.161825 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.20195 (* 0.0454545 = 0.145543 loss) | |
| I0404 14:18:57.161839 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.36012 (* 0.0454545 = 0.152733 loss) | |
| I0404 14:18:57.161854 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.47671 (* 0.0454545 = 0.158032 loss) | |
| I0404 14:18:57.161867 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.93396 (* 0.0454545 = 0.133362 loss) | |
| I0404 14:18:57.161880 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.49751 (* 0.0454545 = 0.113523 loss) | |
| I0404 14:18:57.161895 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.59313 (* 0.0454545 = 0.0724149 loss) | |
| I0404 14:18:57.161907 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.865737 (* 0.0454545 = 0.0393517 loss) | |
| I0404 14:18:57.161921 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.319595 (* 0.0454545 = 0.014527 loss) | |
| I0404 14:18:57.161936 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.231307 (* 0.0454545 = 0.010514 loss) | |
| I0404 14:18:57.161949 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.48692e-05 (* 0.0454545 = 2.03951e-06 loss) | |
| I0404 14:18:57.161964 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.45381e-05 (* 0.0454545 = 2.479e-06 loss) | |
| I0404 14:18:57.161978 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.16595e-05 (* 0.0454545 = 2.34816e-06 loss) | |
| I0404 14:18:57.161993 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.38851e-05 (* 0.0454545 = 1.99478e-06 loss) | |
| I0404 14:18:57.162008 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.09754e-05 (* 0.0454545 = 2.31706e-06 loss) | |
| I0404 14:18:57.162021 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.24714e-05 (* 0.0454545 = 1.93052e-06 loss) | |
| I0404 14:18:57.162035 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.28306e-05 (* 0.0454545 = 1.94684e-06 loss) | |
| I0404 14:18:57.162084 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.93057e-05 (* 0.0454545 = 2.24117e-06 loss) | |
| I0404 14:18:57.162101 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.12929e-05 (* 0.0454545 = 1.87695e-06 loss) | |
| I0404 14:18:57.162118 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.57429e-05 (* 0.0454545 = 2.07922e-06 loss) | |
| I0404 14:18:57.162149 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.61806e-05 (* 0.0454545 = 2.09912e-06 loss) | |
| I0404 14:18:57.162164 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.46602e-05 (* 0.0454545 = 2.03001e-06 loss) | |
| I0404 14:18:57.162176 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:18:57.162189 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000192167 | |
| I0404 14:18:57.162202 9252 sgd_solver.cpp:106] Iteration 38000, lr = 0.00962 | |
| I0404 14:20:08.073794 9252 solver.cpp:229] Iteration 38500, loss = 0.904736 | |
| I0404 14:20:08.073984 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.15625 | |
| I0404 14:20:08.074007 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 14:20:08.074019 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:20:08.074033 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 14:20:08.074044 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.125 | |
| I0404 14:20:08.074057 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.3125 | |
| I0404 14:20:08.074069 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 14:20:08.074080 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:20:08.074092 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:20:08.074105 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:20:08.074115 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:20:08.074127 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:20:08.074139 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:20:08.074151 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:20:08.074162 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:20:08.074174 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:20:08.074187 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:20:08.074198 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:20:08.074209 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:20:08.074221 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:20:08.074232 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:20:08.074244 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:20:08.074259 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.80698 (* 0.0454545 = 0.12759 loss) | |
| I0404 14:20:08.074275 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.23331 (* 0.0454545 = 0.146969 loss) | |
| I0404 14:20:08.074288 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.2709 (* 0.0454545 = 0.148677 loss) | |
| I0404 14:20:08.074311 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.22476 (* 0.0454545 = 0.14658 loss) | |
| I0404 14:20:08.074332 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.1217 (* 0.0454545 = 0.141895 loss) | |
| I0404 14:20:08.074347 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.58151 (* 0.0454545 = 0.117341 loss) | |
| I0404 14:20:08.074362 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.12609 (* 0.0454545 = 0.0511857 loss) | |
| I0404 14:20:08.074375 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.308413 (* 0.0454545 = 0.0140188 loss) | |
| I0404 14:20:08.074389 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.214232 (* 0.0454545 = 0.00973783 loss) | |
| I0404 14:20:08.074404 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.218938 (* 0.0454545 = 0.00995173 loss) | |
| I0404 14:20:08.074419 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.64337e-05 (* 0.0454545 = 3.01971e-06 loss) | |
| I0404 14:20:08.074432 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.45307e-05 (* 0.0454545 = 3.38776e-06 loss) | |
| I0404 14:20:08.074446 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.41658e-05 (* 0.0454545 = 2.91663e-06 loss) | |
| I0404 14:20:08.074460 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.56512e-05 (* 0.0454545 = 2.98415e-06 loss) | |
| I0404 14:20:08.074475 9252 solver.cpp:245] Train net output #36: loss/loss15 = 6.99859e-05 (* 0.0454545 = 3.18118e-06 loss) | |
| I0404 14:20:08.074487 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.51856e-05 (* 0.0454545 = 2.96298e-06 loss) | |
| I0404 14:20:08.074501 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.68545e-05 (* 0.0454545 = 3.03884e-06 loss) | |
| I0404 14:20:08.074533 9252 solver.cpp:245] Train net output #39: loss/loss18 = 6.33624e-05 (* 0.0454545 = 2.88011e-06 loss) | |
| I0404 14:20:08.074548 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.50561e-05 (* 0.0454545 = 2.9571e-06 loss) | |
| I0404 14:20:08.074563 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.53968e-05 (* 0.0454545 = 2.97258e-06 loss) | |
| I0404 14:20:08.074576 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.70215e-05 (* 0.0454545 = 3.04643e-06 loss) | |
| I0404 14:20:08.074590 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.77609e-05 (* 0.0454545 = 3.08004e-06 loss) | |
| I0404 14:20:08.074604 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:20:08.074615 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000112151 | |
| I0404 14:20:08.074630 9252 sgd_solver.cpp:106] Iteration 38500, lr = 0.009615 | |
| I0404 14:21:19.135660 9252 solver.cpp:229] Iteration 39000, loss = 0.902804 | |
| I0404 14:21:19.135812 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.15625 | |
| I0404 14:21:19.135829 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:21:19.135843 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 14:21:19.135856 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 14:21:19.135869 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 14:21:19.135880 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 14:21:19.135892 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 14:21:19.135906 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:21:19.135918 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:21:19.135931 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:21:19.135942 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:21:19.135954 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:21:19.135965 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:21:19.135977 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:21:19.135988 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:21:19.136000 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:21:19.136011 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:21:19.136023 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:21:19.136034 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:21:19.136046 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:21:19.136057 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:21:19.136070 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:21:19.136085 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.48807 (* 0.0454545 = 0.113094 loss) | |
| I0404 14:21:19.136099 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.26946 (* 0.0454545 = 0.148612 loss) | |
| I0404 14:21:19.136113 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.025 (* 0.0454545 = 0.1375 loss) | |
| I0404 14:21:19.136126 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.90505 (* 0.0454545 = 0.132048 loss) | |
| I0404 14:21:19.136140 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.70992 (* 0.0454545 = 0.123178 loss) | |
| I0404 14:21:19.136154 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.04461 (* 0.0454545 = 0.092937 loss) | |
| I0404 14:21:19.136168 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.03438 (* 0.0454545 = 0.0470171 loss) | |
| I0404 14:21:19.136181 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.268509 (* 0.0454545 = 0.0122049 loss) | |
| I0404 14:21:19.136194 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0991103 (* 0.0454545 = 0.00450501 loss) | |
| I0404 14:21:19.136209 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0144248 (* 0.0454545 = 0.000655674 loss) | |
| I0404 14:21:19.136224 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.69113e-05 (* 0.0454545 = 3.04142e-06 loss) | |
| I0404 14:21:19.136237 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.24978e-05 (* 0.0454545 = 3.29536e-06 loss) | |
| I0404 14:21:19.136251 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.70842e-05 (* 0.0454545 = 2.59473e-06 loss) | |
| I0404 14:21:19.136265 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.58224e-05 (* 0.0454545 = 2.99193e-06 loss) | |
| I0404 14:21:19.136279 9252 solver.cpp:245] Train net output #36: loss/loss15 = 6.49043e-05 (* 0.0454545 = 2.9502e-06 loss) | |
| I0404 14:21:19.136293 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.802e-05 (* 0.0454545 = 3.09182e-06 loss) | |
| I0404 14:21:19.136307 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.23645e-05 (* 0.0454545 = 2.83475e-06 loss) | |
| I0404 14:21:19.136338 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.51319e-05 (* 0.0454545 = 2.506e-06 loss) | |
| I0404 14:21:19.136354 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.29348e-05 (* 0.0454545 = 2.86067e-06 loss) | |
| I0404 14:21:19.136368 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.71801e-05 (* 0.0454545 = 2.5991e-06 loss) | |
| I0404 14:21:19.136382 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.61144e-05 (* 0.0454545 = 2.55065e-06 loss) | |
| I0404 14:21:19.136395 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.0697e-05 (* 0.0454545 = 2.75895e-06 loss) | |
| I0404 14:21:19.136407 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:21:19.136420 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000155991 | |
| I0404 14:21:19.136433 9252 sgd_solver.cpp:106] Iteration 39000, lr = 0.00961 | |
| I0404 14:22:30.577646 9252 solver.cpp:229] Iteration 39500, loss = 0.90434 | |
| I0404 14:22:30.577813 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 14:22:30.577833 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:22:30.577847 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 14:22:30.577858 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 14:22:30.577878 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 14:22:30.577890 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 14:22:30.577903 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.875 | |
| I0404 14:22:30.577914 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 14:22:30.577926 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 14:22:30.577937 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:22:30.577950 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:22:30.577960 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:22:30.577972 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:22:30.577992 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:22:30.578004 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:22:30.578016 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:22:30.578027 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:22:30.578037 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:22:30.578049 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:22:30.578060 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:22:30.578073 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:22:30.578083 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:22:30.578099 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.95367 (* 0.0454545 = 0.134258 loss) | |
| I0404 14:22:30.578120 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.32708 (* 0.0454545 = 0.151231 loss) | |
| I0404 14:22:30.578133 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.26963 (* 0.0454545 = 0.14862 loss) | |
| I0404 14:22:30.578147 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.32875 (* 0.0454545 = 0.151307 loss) | |
| I0404 14:22:30.578161 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.10908 (* 0.0454545 = 0.141322 loss) | |
| I0404 14:22:30.578176 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.32282 (* 0.0454545 = 0.105583 loss) | |
| I0404 14:22:30.578188 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.808778 (* 0.0454545 = 0.0367626 loss) | |
| I0404 14:22:30.578202 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.276 (* 0.0454545 = 0.0125455 loss) | |
| I0404 14:22:30.578215 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0194581 (* 0.0454545 = 0.000884457 loss) | |
| I0404 14:22:30.578230 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0047066 (* 0.0454545 = 0.000213936 loss) | |
| I0404 14:22:30.578244 9252 solver.cpp:245] Train net output #32: loss/loss11 = 7.73897e-05 (* 0.0454545 = 3.51772e-06 loss) | |
| I0404 14:22:30.578258 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.80899e-05 (* 0.0454545 = 3.54954e-06 loss) | |
| I0404 14:22:30.578271 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.34796e-05 (* 0.0454545 = 3.33998e-06 loss) | |
| I0404 14:22:30.578285 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.31325e-05 (* 0.0454545 = 3.3242e-06 loss) | |
| I0404 14:22:30.578299 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.83045e-05 (* 0.0454545 = 3.55929e-06 loss) | |
| I0404 14:22:30.578325 9252 solver.cpp:245] Train net output #37: loss/loss16 = 7.43979e-05 (* 0.0454545 = 3.38172e-06 loss) | |
| I0404 14:22:30.578342 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.1715e-05 (* 0.0454545 = 3.25977e-06 loss) | |
| I0404 14:22:30.578371 9252 solver.cpp:245] Train net output #39: loss/loss18 = 6.82167e-05 (* 0.0454545 = 3.10076e-06 loss) | |
| I0404 14:22:30.578385 9252 solver.cpp:245] Train net output #40: loss/loss19 = 7.31913e-05 (* 0.0454545 = 3.32688e-06 loss) | |
| I0404 14:22:30.578399 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.34539e-05 (* 0.0454545 = 3.33881e-06 loss) | |
| I0404 14:22:30.578413 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.43896e-05 (* 0.0454545 = 3.38134e-06 loss) | |
| I0404 14:22:30.578426 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.49236e-05 (* 0.0454545 = 3.40562e-06 loss) | |
| I0404 14:22:30.578438 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:22:30.578449 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000105055 | |
| I0404 14:22:30.578464 9252 sgd_solver.cpp:106] Iteration 39500, lr = 0.009605 | |
| I0404 14:23:41.364197 9252 solver.cpp:338] Iteration 40000, Testing net (#0) | |
| I0404 14:23:49.366588 9252 solver.cpp:393] Test loss: 0.822353 | |
| I0404 14:23:49.366634 9252 solver.cpp:406] Test net output #0: loss/accuracy01 = 0.116 | |
| I0404 14:23:49.366650 9252 solver.cpp:406] Test net output #1: loss/accuracy02 = 0.093 | |
| I0404 14:23:49.366662 9252 solver.cpp:406] Test net output #2: loss/accuracy03 = 0.1 | |
| I0404 14:23:49.366674 9252 solver.cpp:406] Test net output #3: loss/accuracy04 = 0.141 | |
| I0404 14:23:49.366685 9252 solver.cpp:406] Test net output #4: loss/accuracy05 = 0.246 | |
| I0404 14:23:49.366698 9252 solver.cpp:406] Test net output #5: loss/accuracy06 = 0.524 | |
| I0404 14:23:49.366708 9252 solver.cpp:406] Test net output #6: loss/accuracy07 = 0.893 | |
| I0404 14:23:49.366719 9252 solver.cpp:406] Test net output #7: loss/accuracy08 = 0.97 | |
| I0404 14:23:49.366731 9252 solver.cpp:406] Test net output #8: loss/accuracy09 = 0.995 | |
| I0404 14:23:49.366742 9252 solver.cpp:406] Test net output #9: loss/accuracy10 = 0.998 | |
| I0404 14:23:49.366756 9252 solver.cpp:406] Test net output #10: loss/accuracy11 = 1 | |
| I0404 14:23:49.366768 9252 solver.cpp:406] Test net output #11: loss/accuracy12 = 1 | |
| I0404 14:23:49.366780 9252 solver.cpp:406] Test net output #12: loss/accuracy13 = 1 | |
| I0404 14:23:49.366791 9252 solver.cpp:406] Test net output #13: loss/accuracy14 = 1 | |
| I0404 14:23:49.366801 9252 solver.cpp:406] Test net output #14: loss/accuracy15 = 1 | |
| I0404 14:23:49.366812 9252 solver.cpp:406] Test net output #15: loss/accuracy16 = 1 | |
| I0404 14:23:49.366823 9252 solver.cpp:406] Test net output #16: loss/accuracy17 = 1 | |
| I0404 14:23:49.366834 9252 solver.cpp:406] Test net output #17: loss/accuracy18 = 1 | |
| I0404 14:23:49.366845 9252 solver.cpp:406] Test net output #18: loss/accuracy19 = 1 | |
| I0404 14:23:49.366857 9252 solver.cpp:406] Test net output #19: loss/accuracy20 = 1 | |
| I0404 14:23:49.366868 9252 solver.cpp:406] Test net output #20: loss/accuracy21 = 1 | |
| I0404 14:23:49.366878 9252 solver.cpp:406] Test net output #21: loss/accuracy22 = 1 | |
| I0404 14:23:49.366894 9252 solver.cpp:406] Test net output #22: loss/loss01 = 3.01237 (* 0.0454545 = 0.136926 loss) | |
| I0404 14:23:49.366907 9252 solver.cpp:406] Test net output #23: loss/loss02 = 3.12292 (* 0.0454545 = 0.141951 loss) | |
| I0404 14:23:49.366920 9252 solver.cpp:406] Test net output #24: loss/loss03 = 3.18633 (* 0.0454545 = 0.144833 loss) | |
| I0404 14:23:49.366933 9252 solver.cpp:406] Test net output #25: loss/loss04 = 3.08773 (* 0.0454545 = 0.140351 loss) | |
| I0404 14:23:49.366946 9252 solver.cpp:406] Test net output #26: loss/loss05 = 2.89707 (* 0.0454545 = 0.131685 loss) | |
| I0404 14:23:49.366960 9252 solver.cpp:406] Test net output #27: loss/loss06 = 1.94957 (* 0.0454545 = 0.088617 loss) | |
| I0404 14:23:49.366973 9252 solver.cpp:406] Test net output #28: loss/loss07 = 0.549658 (* 0.0454545 = 0.0249845 loss) | |
| I0404 14:23:49.366986 9252 solver.cpp:406] Test net output #29: loss/loss08 = 0.20798 (* 0.0454545 = 0.00945364 loss) | |
| I0404 14:23:49.366999 9252 solver.cpp:406] Test net output #30: loss/loss09 = 0.0498528 (* 0.0454545 = 0.00226604 loss) | |
| I0404 14:23:49.367013 9252 solver.cpp:406] Test net output #31: loss/loss10 = 0.0262456 (* 0.0454545 = 0.00119298 loss) | |
| I0404 14:23:49.367027 9252 solver.cpp:406] Test net output #32: loss/loss11 = 0.000167756 (* 0.0454545 = 7.62528e-06 loss) | |
| I0404 14:23:49.367041 9252 solver.cpp:406] Test net output #33: loss/loss12 = 0.000178635 (* 0.0454545 = 8.11978e-06 loss) | |
| I0404 14:23:49.367055 9252 solver.cpp:406] Test net output #34: loss/loss13 = 0.000164635 (* 0.0454545 = 7.48343e-06 loss) | |
| I0404 14:23:49.367069 9252 solver.cpp:406] Test net output #35: loss/loss14 = 0.000173347 (* 0.0454545 = 7.8794e-06 loss) | |
| I0404 14:23:49.367081 9252 solver.cpp:406] Test net output #36: loss/loss15 = 0.000168951 (* 0.0454545 = 7.67957e-06 loss) | |
| I0404 14:23:49.367095 9252 solver.cpp:406] Test net output #37: loss/loss16 = 0.000181074 (* 0.0454545 = 8.23062e-06 loss) | |
| I0404 14:23:49.367108 9252 solver.cpp:406] Test net output #38: loss/loss17 = 0.0001662 (* 0.0454545 = 7.55454e-06 loss) | |
| I0404 14:23:49.367156 9252 solver.cpp:406] Test net output #39: loss/loss18 = 0.000152265 (* 0.0454545 = 6.92115e-06 loss) | |
| I0404 14:23:49.367172 9252 solver.cpp:406] Test net output #40: loss/loss19 = 0.000182612 (* 0.0454545 = 8.30054e-06 loss) | |
| I0404 14:23:49.367184 9252 solver.cpp:406] Test net output #41: loss/loss20 = 0.000172973 (* 0.0454545 = 7.8624e-06 loss) | |
| I0404 14:23:49.367198 9252 solver.cpp:406] Test net output #42: loss/loss21 = 0.000172998 (* 0.0454545 = 7.86353e-06 loss) | |
| I0404 14:23:49.367211 9252 solver.cpp:406] Test net output #43: loss/loss22 = 0.000166995 (* 0.0454545 = 7.59067e-06 loss) | |
| I0404 14:23:49.367223 9252 solver.cpp:406] Test net output #44: total_accuracy = 0 | |
| I0404 14:23:49.367234 9252 solver.cpp:406] Test net output #45: total_confidence = 0.000104316 | |
| I0404 14:23:49.401162 9252 solver.cpp:229] Iteration 40000, loss = 0.900159 | |
| I0404 14:23:49.401202 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 14:23:49.401219 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 14:23:49.401232 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 14:23:49.401244 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 14:23:49.401257 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 14:23:49.401268 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 14:23:49.401279 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.65625 | |
| I0404 14:23:49.401291 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.75 | |
| I0404 14:23:49.401303 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.875 | |
| I0404 14:23:49.401314 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:23:49.401326 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:23:49.401337 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:23:49.401348 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:23:49.401360 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:23:49.401371 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:23:49.401382 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:23:49.401393 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:23:49.401406 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:23:49.401428 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:23:49.401445 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:23:49.401456 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:23:49.401468 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:23:49.401482 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.86705 (* 0.0454545 = 0.13032 loss) | |
| I0404 14:23:49.401496 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.3384 (* 0.0454545 = 0.151745 loss) | |
| I0404 14:23:49.401510 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.21012 (* 0.0454545 = 0.145914 loss) | |
| I0404 14:23:49.401525 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.28832 (* 0.0454545 = 0.149469 loss) | |
| I0404 14:23:49.401538 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.92341 (* 0.0454545 = 0.132882 loss) | |
| I0404 14:23:49.401551 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.49649 (* 0.0454545 = 0.113477 loss) | |
| I0404 14:23:49.401566 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.53265 (* 0.0454545 = 0.0696659 loss) | |
| I0404 14:23:49.401578 9252 solver.cpp:245] Train net output #29: loss/loss08 = 1.09698 (* 0.0454545 = 0.0498628 loss) | |
| I0404 14:23:49.401592 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.538732 (* 0.0454545 = 0.0244878 loss) | |
| I0404 14:23:49.401610 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.122241 (* 0.0454545 = 0.00555643 loss) | |
| I0404 14:23:49.401643 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000105588 (* 0.0454545 = 4.79944e-06 loss) | |
| I0404 14:23:49.401659 9252 solver.cpp:245] Train net output #33: loss/loss12 = 9.96951e-05 (* 0.0454545 = 4.5316e-06 loss) | |
| I0404 14:23:49.401672 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000102353 (* 0.0454545 = 4.65242e-06 loss) | |
| I0404 14:23:49.401686 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000104541 (* 0.0454545 = 4.75185e-06 loss) | |
| I0404 14:23:49.401700 9252 solver.cpp:245] Train net output #36: loss/loss15 = 9.2907e-05 (* 0.0454545 = 4.22304e-06 loss) | |
| I0404 14:23:49.401715 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000106594 (* 0.0454545 = 4.84518e-06 loss) | |
| I0404 14:23:49.401727 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000100213 (* 0.0454545 = 4.55512e-06 loss) | |
| I0404 14:23:49.401741 9252 solver.cpp:245] Train net output #39: loss/loss18 = 9.39866e-05 (* 0.0454545 = 4.27212e-06 loss) | |
| I0404 14:23:49.401756 9252 solver.cpp:245] Train net output #40: loss/loss19 = 9.60735e-05 (* 0.0454545 = 4.36698e-06 loss) | |
| I0404 14:23:49.401769 9252 solver.cpp:245] Train net output #41: loss/loss20 = 9.05371e-05 (* 0.0454545 = 4.11532e-06 loss) | |
| I0404 14:23:49.401783 9252 solver.cpp:245] Train net output #42: loss/loss21 = 9.67404e-05 (* 0.0454545 = 4.39729e-06 loss) | |
| I0404 14:23:49.401796 9252 solver.cpp:245] Train net output #43: loss/loss22 = 9.74647e-05 (* 0.0454545 = 4.43021e-06 loss) | |
| I0404 14:23:49.401808 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:23:49.401820 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00031639 | |
| I0404 14:23:49.401835 9252 sgd_solver.cpp:106] Iteration 40000, lr = 0.0096 | |
| I0404 14:25:00.031783 9252 solver.cpp:229] Iteration 40500, loss = 0.898315 | |
| I0404 14:25:00.031905 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.21875 | |
| I0404 14:25:00.031924 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 14:25:00.031936 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 14:25:00.031949 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 14:25:00.031961 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 14:25:00.031973 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 14:25:00.031985 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 14:25:00.031996 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:25:00.032008 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:25:00.032021 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:25:00.032032 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:25:00.032043 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:25:00.032055 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:25:00.032066 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:25:00.032078 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:25:00.032090 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:25:00.032101 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:25:00.032112 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:25:00.032124 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:25:00.032135 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:25:00.032146 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:25:00.032158 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:25:00.032173 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.93288 (* 0.0454545 = 0.133313 loss) | |
| I0404 14:25:00.032187 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.34785 (* 0.0454545 = 0.152175 loss) | |
| I0404 14:25:00.032202 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.32643 (* 0.0454545 = 0.151201 loss) | |
| I0404 14:25:00.032215 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.61058 (* 0.0454545 = 0.164117 loss) | |
| I0404 14:25:00.032229 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.17152 (* 0.0454545 = 0.14416 loss) | |
| I0404 14:25:00.032243 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.18337 (* 0.0454545 = 0.0992441 loss) | |
| I0404 14:25:00.032258 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.07812 (* 0.0454545 = 0.0490057 loss) | |
| I0404 14:25:00.032271 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.560238 (* 0.0454545 = 0.0254654 loss) | |
| I0404 14:25:00.032285 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.368579 (* 0.0454545 = 0.0167536 loss) | |
| I0404 14:25:00.032299 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.113816 (* 0.0454545 = 0.00517343 loss) | |
| I0404 14:25:00.032315 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000209059 (* 0.0454545 = 9.50268e-06 loss) | |
| I0404 14:25:00.032327 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000195485 (* 0.0454545 = 8.88568e-06 loss) | |
| I0404 14:25:00.032341 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000224602 (* 0.0454545 = 1.02092e-05 loss) | |
| I0404 14:25:00.032356 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000221335 (* 0.0454545 = 1.00607e-05 loss) | |
| I0404 14:25:00.032369 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000192257 (* 0.0454545 = 8.73897e-06 loss) | |
| I0404 14:25:00.032383 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000222507 (* 0.0454545 = 1.01139e-05 loss) | |
| I0404 14:25:00.032397 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000188776 (* 0.0454545 = 8.58074e-06 loss) | |
| I0404 14:25:00.032428 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000197502 (* 0.0454545 = 8.97737e-06 loss) | |
| I0404 14:25:00.032444 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.0002043 (* 0.0454545 = 9.28634e-06 loss) | |
| I0404 14:25:00.032457 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000196507 (* 0.0454545 = 8.93212e-06 loss) | |
| I0404 14:25:00.032471 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000202525 (* 0.0454545 = 9.20568e-06 loss) | |
| I0404 14:25:00.032485 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000202787 (* 0.0454545 = 9.21759e-06 loss) | |
| I0404 14:25:00.032497 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:25:00.032508 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000302297 | |
| I0404 14:25:00.032521 9252 sgd_solver.cpp:106] Iteration 40500, lr = 0.009595 | |
| I0404 14:26:11.141767 9252 solver.cpp:229] Iteration 41000, loss = 0.893983 | |
| I0404 14:26:11.142043 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.28125 | |
| I0404 14:26:11.142063 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 14:26:11.142076 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 14:26:11.142089 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 14:26:11.142102 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 14:26:11.142113 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 14:26:11.142125 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.5625 | |
| I0404 14:26:11.142138 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 14:26:11.142150 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:26:11.142163 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:26:11.142174 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:26:11.142186 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:26:11.142197 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:26:11.142210 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:26:11.142220 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:26:11.142247 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:26:11.142261 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:26:11.142272 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:26:11.142283 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:26:11.142295 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:26:11.142309 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:26:11.142321 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:26:11.142336 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.69355 (* 0.0454545 = 0.122434 loss) | |
| I0404 14:26:11.142351 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.90733 (* 0.0454545 = 0.132151 loss) | |
| I0404 14:26:11.142365 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.07115 (* 0.0454545 = 0.139598 loss) | |
| I0404 14:26:11.142379 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.09093 (* 0.0454545 = 0.140497 loss) | |
| I0404 14:26:11.142392 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.66603 (* 0.0454545 = 0.121183 loss) | |
| I0404 14:26:11.142406 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.40569 (* 0.0454545 = 0.109349 loss) | |
| I0404 14:26:11.142419 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.877 (* 0.0454545 = 0.0853181 loss) | |
| I0404 14:26:11.142433 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.350012 (* 0.0454545 = 0.0159096 loss) | |
| I0404 14:26:11.142447 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.347312 (* 0.0454545 = 0.0157869 loss) | |
| I0404 14:26:11.142462 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0331523 (* 0.0454545 = 0.00150692 loss) | |
| I0404 14:26:11.142477 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000111987 (* 0.0454545 = 5.09032e-06 loss) | |
| I0404 14:26:11.142490 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000111805 (* 0.0454545 = 5.08204e-06 loss) | |
| I0404 14:26:11.142505 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000109022 (* 0.0454545 = 4.95554e-06 loss) | |
| I0404 14:26:11.142519 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000103964 (* 0.0454545 = 4.72565e-06 loss) | |
| I0404 14:26:11.142534 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000103087 (* 0.0454545 = 4.68577e-06 loss) | |
| I0404 14:26:11.142547 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000100948 (* 0.0454545 = 4.58857e-06 loss) | |
| I0404 14:26:11.142561 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.00010702 (* 0.0454545 = 4.86454e-06 loss) | |
| I0404 14:26:11.142590 9252 solver.cpp:245] Train net output #39: loss/loss18 = 9.66389e-05 (* 0.0454545 = 4.39268e-06 loss) | |
| I0404 14:26:11.142611 9252 solver.cpp:245] Train net output #40: loss/loss19 = 9.9913e-05 (* 0.0454545 = 4.5415e-06 loss) | |
| I0404 14:26:11.142627 9252 solver.cpp:245] Train net output #41: loss/loss20 = 9.53193e-05 (* 0.0454545 = 4.3327e-06 loss) | |
| I0404 14:26:11.142642 9252 solver.cpp:245] Train net output #42: loss/loss21 = 9.08254e-05 (* 0.0454545 = 4.12843e-06 loss) | |
| I0404 14:26:11.142660 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000101941 (* 0.0454545 = 4.63368e-06 loss) | |
| I0404 14:26:11.142673 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:26:11.142683 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000232007 | |
| I0404 14:26:11.142696 9252 sgd_solver.cpp:106] Iteration 41000, lr = 0.00959 | |
| I0404 14:27:21.915628 9252 solver.cpp:229] Iteration 41500, loss = 0.895001 | |
| I0404 14:27:21.915766 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.28125 | |
| I0404 14:27:21.915786 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 14:27:21.915798 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 14:27:21.915809 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 14:27:21.915822 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 14:27:21.915833 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.21875 | |
| I0404 14:27:21.915845 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.53125 | |
| I0404 14:27:21.915856 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 14:27:21.915868 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 14:27:21.915880 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:27:21.915894 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:27:21.915904 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:27:21.915916 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:27:21.915927 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:27:21.915938 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:27:21.915951 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:27:21.915961 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:27:21.915972 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:27:21.915984 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:27:21.915995 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:27:21.916007 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:27:21.916018 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:27:21.916034 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.54438 (* 0.0454545 = 0.115654 loss) | |
| I0404 14:27:21.916049 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.10438 (* 0.0454545 = 0.141108 loss) | |
| I0404 14:27:21.916062 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.02354 (* 0.0454545 = 0.137434 loss) | |
| I0404 14:27:21.916076 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.0678 (* 0.0454545 = 0.139446 loss) | |
| I0404 14:27:21.916090 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.96523 (* 0.0454545 = 0.134783 loss) | |
| I0404 14:27:21.916103 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.99447 (* 0.0454545 = 0.136112 loss) | |
| I0404 14:27:21.916116 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.79293 (* 0.0454545 = 0.0814967 loss) | |
| I0404 14:27:21.916131 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.396385 (* 0.0454545 = 0.0180175 loss) | |
| I0404 14:27:21.916146 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.430348 (* 0.0454545 = 0.0195613 loss) | |
| I0404 14:27:21.916160 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0157196 (* 0.0454545 = 0.000714528 loss) | |
| I0404 14:27:21.916174 9252 solver.cpp:245] Train net output #32: loss/loss11 = 5.40011e-05 (* 0.0454545 = 2.4546e-06 loss) | |
| I0404 14:27:21.916189 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.17384e-05 (* 0.0454545 = 2.35175e-06 loss) | |
| I0404 14:27:21.916208 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.92842e-05 (* 0.0454545 = 2.24019e-06 loss) | |
| I0404 14:27:21.916223 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.24103e-05 (* 0.0454545 = 2.38229e-06 loss) | |
| I0404 14:27:21.916236 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.15669e-05 (* 0.0454545 = 2.34395e-06 loss) | |
| I0404 14:27:21.916249 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.77022e-05 (* 0.0454545 = 2.16828e-06 loss) | |
| I0404 14:27:21.916263 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.93128e-05 (* 0.0454545 = 2.24149e-06 loss) | |
| I0404 14:27:21.916296 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.7589e-05 (* 0.0454545 = 2.16314e-06 loss) | |
| I0404 14:27:21.916311 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.00993e-05 (* 0.0454545 = 2.27724e-06 loss) | |
| I0404 14:27:21.916324 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.82664e-05 (* 0.0454545 = 2.19393e-06 loss) | |
| I0404 14:27:21.916338 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.66941e-05 (* 0.0454545 = 2.12246e-06 loss) | |
| I0404 14:27:21.916352 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.82624e-05 (* 0.0454545 = 2.19375e-06 loss) | |
| I0404 14:27:21.916363 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:27:21.916375 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000268182 | |
| I0404 14:27:21.916388 9252 sgd_solver.cpp:106] Iteration 41500, lr = 0.009585 | |
| I0404 14:28:32.965742 9252 solver.cpp:229] Iteration 42000, loss = 0.895006 | |
| I0404 14:28:32.965862 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.125 | |
| I0404 14:28:32.965881 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 14:28:32.965894 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:28:32.965906 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 14:28:32.965919 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 14:28:32.965930 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 14:28:32.965942 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 14:28:32.965955 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:28:32.965966 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:28:32.965978 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:28:32.965991 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:28:32.966001 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:28:32.966013 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:28:32.966024 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:28:32.966035 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:28:32.966048 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:28:32.966058 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:28:32.966070 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:28:32.966081 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:28:32.966092 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:28:32.966104 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:28:32.966115 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:28:32.966131 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.74084 (* 0.0454545 = 0.124584 loss) | |
| I0404 14:28:32.966145 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.01653 (* 0.0454545 = 0.137115 loss) | |
| I0404 14:28:32.966159 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.05982 (* 0.0454545 = 0.139083 loss) | |
| I0404 14:28:32.966173 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.03405 (* 0.0454545 = 0.137911 loss) | |
| I0404 14:28:32.966187 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.39346 (* 0.0454545 = 0.108794 loss) | |
| I0404 14:28:32.966200 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.09962 (* 0.0454545 = 0.0954374 loss) | |
| I0404 14:28:32.966213 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.07641 (* 0.0454545 = 0.0489277 loss) | |
| I0404 14:28:32.966228 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.578565 (* 0.0454545 = 0.0262984 loss) | |
| I0404 14:28:32.966241 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.284962 (* 0.0454545 = 0.0129528 loss) | |
| I0404 14:28:32.966255 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0105527 (* 0.0454545 = 0.000479666 loss) | |
| I0404 14:28:32.966269 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.92352e-05 (* 0.0454545 = 1.78342e-06 loss) | |
| I0404 14:28:32.966284 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.10072e-05 (* 0.0454545 = 1.86396e-06 loss) | |
| I0404 14:28:32.966297 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.87492e-05 (* 0.0454545 = 1.76133e-06 loss) | |
| I0404 14:28:32.966311 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.69346e-05 (* 0.0454545 = 1.67885e-06 loss) | |
| I0404 14:28:32.966325 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.92035e-05 (* 0.0454545 = 1.78198e-06 loss) | |
| I0404 14:28:32.966338 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.74299e-05 (* 0.0454545 = 1.70136e-06 loss) | |
| I0404 14:28:32.966352 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.52076e-05 (* 0.0454545 = 1.60035e-06 loss) | |
| I0404 14:28:32.966383 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.39354e-05 (* 0.0454545 = 1.54252e-06 loss) | |
| I0404 14:28:32.966398 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.64315e-05 (* 0.0454545 = 1.65598e-06 loss) | |
| I0404 14:28:32.966413 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.60646e-05 (* 0.0454545 = 1.6393e-06 loss) | |
| I0404 14:28:32.966426 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.4511e-05 (* 0.0454545 = 1.56868e-06 loss) | |
| I0404 14:28:32.966440 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.73369e-05 (* 0.0454545 = 1.69713e-06 loss) | |
| I0404 14:28:32.966452 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:28:32.966464 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000131817 | |
| I0404 14:28:32.966477 9252 sgd_solver.cpp:106] Iteration 42000, lr = 0.00958 | |
| I0404 14:29:43.957317 9252 solver.cpp:229] Iteration 42500, loss = 0.891995 | |
| I0404 14:29:43.957463 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.28125 | |
| I0404 14:29:43.957482 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:29:43.957495 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 14:29:43.957507 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 14:29:43.957520 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 14:29:43.957531 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 14:29:43.957543 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 14:29:43.957556 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 14:29:43.957567 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 14:29:43.957579 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:29:43.957592 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:29:43.957602 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:29:43.957614 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:29:43.957626 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:29:43.957638 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:29:43.957649 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:29:43.957660 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:29:43.957672 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:29:43.957684 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:29:43.957695 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:29:43.957707 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:29:43.957718 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:29:43.957734 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.4931 (* 0.0454545 = 0.113323 loss) | |
| I0404 14:29:43.957751 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.87215 (* 0.0454545 = 0.130552 loss) | |
| I0404 14:29:43.957765 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.95597 (* 0.0454545 = 0.134362 loss) | |
| I0404 14:29:43.957779 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.20686 (* 0.0454545 = 0.145766 loss) | |
| I0404 14:29:43.957792 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.65861 (* 0.0454545 = 0.120846 loss) | |
| I0404 14:29:43.957806 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.83784 (* 0.0454545 = 0.0835383 loss) | |
| I0404 14:29:43.957820 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.792449 (* 0.0454545 = 0.0360204 loss) | |
| I0404 14:29:43.957834 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.126498 (* 0.0454545 = 0.00574991 loss) | |
| I0404 14:29:43.957849 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.00957432 (* 0.0454545 = 0.000435197 loss) | |
| I0404 14:29:43.957862 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00279894 (* 0.0454545 = 0.000127224 loss) | |
| I0404 14:29:43.957876 9252 solver.cpp:245] Train net output #32: loss/loss11 = 7.09846e-05 (* 0.0454545 = 3.22657e-06 loss) | |
| I0404 14:29:43.957890 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.07724e-05 (* 0.0454545 = 3.21693e-06 loss) | |
| I0404 14:29:43.957904 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.50836e-05 (* 0.0454545 = 2.95835e-06 loss) | |
| I0404 14:29:43.957918 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.77993e-05 (* 0.0454545 = 3.08179e-06 loss) | |
| I0404 14:29:43.957932 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.05884e-05 (* 0.0454545 = 3.20856e-06 loss) | |
| I0404 14:29:43.957947 9252 solver.cpp:245] Train net output #37: loss/loss16 = 7.91312e-05 (* 0.0454545 = 3.59687e-06 loss) | |
| I0404 14:29:43.957960 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.91441e-05 (* 0.0454545 = 3.14291e-06 loss) | |
| I0404 14:29:43.957993 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.90314e-05 (* 0.0454545 = 2.68325e-06 loss) | |
| I0404 14:29:43.958009 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.89917e-05 (* 0.0454545 = 3.13599e-06 loss) | |
| I0404 14:29:43.958022 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.32266e-05 (* 0.0454545 = 2.87393e-06 loss) | |
| I0404 14:29:43.958036 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.43593e-05 (* 0.0454545 = 2.92542e-06 loss) | |
| I0404 14:29:43.958050 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.44622e-05 (* 0.0454545 = 2.9301e-06 loss) | |
| I0404 14:29:43.958062 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:29:43.958073 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00026189 | |
| I0404 14:29:43.958088 9252 sgd_solver.cpp:106] Iteration 42500, lr = 0.009575 | |
| I0404 14:30:55.344177 9252 solver.cpp:229] Iteration 43000, loss = 0.888486 | |
| I0404 14:30:55.344350 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 14:30:55.344379 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:30:55.344406 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:30:55.344434 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 14:30:55.344455 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 14:30:55.344467 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 14:30:55.344480 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 14:30:55.344491 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 14:30:55.344503 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 14:30:55.344516 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:30:55.344527 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:30:55.344543 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:30:55.344561 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:30:55.344574 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:30:55.344586 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:30:55.344599 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:30:55.344610 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:30:55.344621 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:30:55.344633 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:30:55.344646 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:30:55.344660 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:30:55.344672 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:30:55.344688 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.78669 (* 0.0454545 = 0.126668 loss) | |
| I0404 14:30:55.344702 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.38253 (* 0.0454545 = 0.153752 loss) | |
| I0404 14:30:55.344717 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.09866 (* 0.0454545 = 0.140848 loss) | |
| I0404 14:30:55.344730 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.26318 (* 0.0454545 = 0.148326 loss) | |
| I0404 14:30:55.344748 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.91469 (* 0.0454545 = 0.132486 loss) | |
| I0404 14:30:55.344775 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.37141 (* 0.0454545 = 0.107791 loss) | |
| I0404 14:30:55.344807 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.982485 (* 0.0454545 = 0.0446584 loss) | |
| I0404 14:30:55.344828 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.275917 (* 0.0454545 = 0.0125417 loss) | |
| I0404 14:30:55.344843 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0451808 (* 0.0454545 = 0.00205367 loss) | |
| I0404 14:30:55.344858 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00912156 (* 0.0454545 = 0.000414617 loss) | |
| I0404 14:30:55.344877 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000281635 (* 0.0454545 = 1.28016e-05 loss) | |
| I0404 14:30:55.344892 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000258315 (* 0.0454545 = 1.17416e-05 loss) | |
| I0404 14:30:55.344905 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000256014 (* 0.0454545 = 1.1637e-05 loss) | |
| I0404 14:30:55.344930 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000276257 (* 0.0454545 = 1.25571e-05 loss) | |
| I0404 14:30:55.344947 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000251895 (* 0.0454545 = 1.14498e-05 loss) | |
| I0404 14:30:55.344961 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000292109 (* 0.0454545 = 1.32777e-05 loss) | |
| I0404 14:30:55.344975 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000251118 (* 0.0454545 = 1.14144e-05 loss) | |
| I0404 14:30:55.345005 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000253999 (* 0.0454545 = 1.15454e-05 loss) | |
| I0404 14:30:55.345021 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000311948 (* 0.0454545 = 1.41795e-05 loss) | |
| I0404 14:30:55.345034 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000297975 (* 0.0454545 = 1.35443e-05 loss) | |
| I0404 14:30:55.345054 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000285161 (* 0.0454545 = 1.29619e-05 loss) | |
| I0404 14:30:55.345085 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000254863 (* 0.0454545 = 1.15847e-05 loss) | |
| I0404 14:30:55.345113 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:30:55.345129 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000110794 | |
| I0404 14:30:55.345144 9252 sgd_solver.cpp:106] Iteration 43000, lr = 0.00957 | |
| I0404 14:32:07.054617 9252 solver.cpp:229] Iteration 43500, loss = 0.882918 | |
| I0404 14:32:07.054767 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.21875 | |
| I0404 14:32:07.054795 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 14:32:07.054811 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 14:32:07.054822 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 14:32:07.054834 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 14:32:07.054847 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 14:32:07.054858 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 14:32:07.054870 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:32:07.054883 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:32:07.054894 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:32:07.054906 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:32:07.054919 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:32:07.054929 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:32:07.054941 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:32:07.054954 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:32:07.054965 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:32:07.054977 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:32:07.054988 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:32:07.055001 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:32:07.055012 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:32:07.055023 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:32:07.055035 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:32:07.055059 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.85519 (* 0.0454545 = 0.129781 loss) | |
| I0404 14:32:07.055074 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.14061 (* 0.0454545 = 0.142755 loss) | |
| I0404 14:32:07.055088 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.99947 (* 0.0454545 = 0.136339 loss) | |
| I0404 14:32:07.055101 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.65602 (* 0.0454545 = 0.166183 loss) | |
| I0404 14:32:07.055115 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.5784 (* 0.0454545 = 0.1172 loss) | |
| I0404 14:32:07.055130 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.01579 (* 0.0454545 = 0.0916268 loss) | |
| I0404 14:32:07.055143 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.778305 (* 0.0454545 = 0.0353775 loss) | |
| I0404 14:32:07.055156 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.310367 (* 0.0454545 = 0.0141076 loss) | |
| I0404 14:32:07.055177 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.10657 (* 0.0454545 = 0.00484408 loss) | |
| I0404 14:32:07.055192 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0312713 (* 0.0454545 = 0.00142142 loss) | |
| I0404 14:32:07.055207 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.96037e-05 (* 0.0454545 = 1.80017e-06 loss) | |
| I0404 14:32:07.055220 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.76386e-05 (* 0.0454545 = 1.71084e-06 loss) | |
| I0404 14:32:07.055234 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.50566e-05 (* 0.0454545 = 1.59348e-06 loss) | |
| I0404 14:32:07.055248 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.05559e-05 (* 0.0454545 = 1.84345e-06 loss) | |
| I0404 14:32:07.055269 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.72287e-05 (* 0.0454545 = 1.69221e-06 loss) | |
| I0404 14:32:07.055284 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.93376e-05 (* 0.0454545 = 1.78807e-06 loss) | |
| I0404 14:32:07.055296 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.54237e-05 (* 0.0454545 = 1.61017e-06 loss) | |
| I0404 14:32:07.055337 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.9103e-05 (* 0.0454545 = 1.32286e-06 loss) | |
| I0404 14:32:07.055352 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.54629e-05 (* 0.0454545 = 1.61195e-06 loss) | |
| I0404 14:32:07.055367 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.47177e-05 (* 0.0454545 = 1.57808e-06 loss) | |
| I0404 14:32:07.055382 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.28996e-05 (* 0.0454545 = 1.49544e-06 loss) | |
| I0404 14:32:07.055394 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.39987e-05 (* 0.0454545 = 1.54539e-06 loss) | |
| I0404 14:32:07.055407 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:32:07.055418 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000274267 | |
| I0404 14:32:07.055433 9252 sgd_solver.cpp:106] Iteration 43500, lr = 0.009565 | |
| I0404 14:33:18.915789 9252 solver.cpp:229] Iteration 44000, loss = 0.882595 | |
| I0404 14:33:18.915938 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.3125 | |
| I0404 14:33:18.915959 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:33:18.915972 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 14:33:18.915984 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 14:33:18.915997 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 14:33:18.916009 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 14:33:18.916020 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.53125 | |
| I0404 14:33:18.916033 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:33:18.916044 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:33:18.916056 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:33:18.916067 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:33:18.916079 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:33:18.916091 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:33:18.916102 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:33:18.916115 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:33:18.916126 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:33:18.916137 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:33:18.916148 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:33:18.916160 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:33:18.916172 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:33:18.916184 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:33:18.916195 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:33:18.916211 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.34756 (* 0.0454545 = 0.106707 loss) | |
| I0404 14:33:18.916226 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.85215 (* 0.0454545 = 0.129643 loss) | |
| I0404 14:33:18.916240 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.0086 (* 0.0454545 = 0.136755 loss) | |
| I0404 14:33:18.916254 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.19026 (* 0.0454545 = 0.145012 loss) | |
| I0404 14:33:18.916268 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.74381 (* 0.0454545 = 0.124719 loss) | |
| I0404 14:33:18.916281 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.41465 (* 0.0454545 = 0.109757 loss) | |
| I0404 14:33:18.916296 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.83723 (* 0.0454545 = 0.0835106 loss) | |
| I0404 14:33:18.916308 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.684337 (* 0.0454545 = 0.0311062 loss) | |
| I0404 14:33:18.916322 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.336323 (* 0.0454545 = 0.0152874 loss) | |
| I0404 14:33:18.916337 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0496905 (* 0.0454545 = 0.00225866 loss) | |
| I0404 14:33:18.916352 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000123602 (* 0.0454545 = 5.61826e-06 loss) | |
| I0404 14:33:18.916365 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000139223 (* 0.0454545 = 6.32831e-06 loss) | |
| I0404 14:33:18.916379 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000122347 (* 0.0454545 = 5.56124e-06 loss) | |
| I0404 14:33:18.916393 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000121025 (* 0.0454545 = 5.50116e-06 loss) | |
| I0404 14:33:18.916406 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000118404 (* 0.0454545 = 5.38201e-06 loss) | |
| I0404 14:33:18.916420 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000118949 (* 0.0454545 = 5.40678e-06 loss) | |
| I0404 14:33:18.916435 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000112136 (* 0.0454545 = 5.09709e-06 loss) | |
| I0404 14:33:18.916465 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000107844 (* 0.0454545 = 4.90198e-06 loss) | |
| I0404 14:33:18.916481 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000114206 (* 0.0454545 = 5.1912e-06 loss) | |
| I0404 14:33:18.916494 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.00011176 (* 0.0454545 = 5.08002e-06 loss) | |
| I0404 14:33:18.916508 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000100214 (* 0.0454545 = 4.55519e-06 loss) | |
| I0404 14:33:18.916522 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000113125 (* 0.0454545 = 5.14204e-06 loss) | |
| I0404 14:33:18.916534 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:33:18.916546 9252 solver.cpp:245] Train net output #45: total_confidence = 6.17661e-05 | |
| I0404 14:33:18.916560 9252 sgd_solver.cpp:106] Iteration 44000, lr = 0.00956 | |
| I0404 14:34:29.969666 9252 solver.cpp:229] Iteration 44500, loss = 0.880826 | |
| I0404 14:34:29.969808 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 14:34:29.969827 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 14:34:29.969841 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 14:34:29.969853 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 14:34:29.969866 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 14:34:29.969877 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.28125 | |
| I0404 14:34:29.969889 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.65625 | |
| I0404 14:34:29.969900 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 14:34:29.969913 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 14:34:29.969924 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:34:29.969936 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:34:29.969949 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:34:29.969960 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:34:29.969971 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:34:29.969983 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:34:29.969995 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:34:29.970006 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:34:29.970018 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:34:29.970029 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:34:29.970041 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:34:29.970052 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:34:29.970064 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:34:29.970080 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.60628 (* 0.0454545 = 0.118467 loss) | |
| I0404 14:34:29.970094 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.12165 (* 0.0454545 = 0.141893 loss) | |
| I0404 14:34:29.970109 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.11979 (* 0.0454545 = 0.141809 loss) | |
| I0404 14:34:29.970139 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.13037 (* 0.0454545 = 0.14229 loss) | |
| I0404 14:34:29.970155 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.0628 (* 0.0454545 = 0.139218 loss) | |
| I0404 14:34:29.970168 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.62389 (* 0.0454545 = 0.119268 loss) | |
| I0404 14:34:29.970182 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.48089 (* 0.0454545 = 0.0673132 loss) | |
| I0404 14:34:29.970196 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.635629 (* 0.0454545 = 0.0288922 loss) | |
| I0404 14:34:29.970211 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.529251 (* 0.0454545 = 0.0240569 loss) | |
| I0404 14:34:29.970224 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.161397 (* 0.0454545 = 0.00733622 loss) | |
| I0404 14:34:29.970238 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.52397e-05 (* 0.0454545 = 1.14726e-06 loss) | |
| I0404 14:34:29.970252 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.55117e-05 (* 0.0454545 = 1.15962e-06 loss) | |
| I0404 14:34:29.970268 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.43754e-05 (* 0.0454545 = 1.10797e-06 loss) | |
| I0404 14:34:29.970281 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.44611e-05 (* 0.0454545 = 1.11187e-06 loss) | |
| I0404 14:34:29.970295 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.32279e-05 (* 0.0454545 = 1.05581e-06 loss) | |
| I0404 14:34:29.970309 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.18233e-05 (* 0.0454545 = 9.9197e-07 loss) | |
| I0404 14:34:29.970324 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.23412e-05 (* 0.0454545 = 1.01551e-06 loss) | |
| I0404 14:34:29.970355 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.2304e-05 (* 0.0454545 = 1.01382e-06 loss) | |
| I0404 14:34:29.970369 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.31236e-05 (* 0.0454545 = 1.05107e-06 loss) | |
| I0404 14:34:29.970392 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.17749e-05 (* 0.0454545 = 9.8977e-07 loss) | |
| I0404 14:34:29.970407 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.28181e-05 (* 0.0454545 = 1.03719e-06 loss) | |
| I0404 14:34:29.970420 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.20394e-05 (* 0.0454545 = 1.00179e-06 loss) | |
| I0404 14:34:29.970432 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:34:29.970443 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000101455 | |
| I0404 14:34:29.970458 9252 sgd_solver.cpp:106] Iteration 44500, lr = 0.009555 | |
| I0404 14:35:41.407841 9252 solver.cpp:229] Iteration 45000, loss = 0.881825 | |
| I0404 14:35:41.408097 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.28125 | |
| I0404 14:35:41.408118 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:35:41.408131 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 14:35:41.408144 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 14:35:41.408156 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 14:35:41.408169 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 14:35:41.408180 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 14:35:41.408192 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 14:35:41.408205 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:35:41.408217 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:35:41.408229 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:35:41.408241 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:35:41.408253 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:35:41.408264 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:35:41.408277 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:35:41.408288 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:35:41.408300 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:35:41.408311 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:35:41.408331 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:35:41.408344 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:35:41.408355 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:35:41.408367 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:35:41.408385 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.56819 (* 0.0454545 = 0.116736 loss) | |
| I0404 14:35:41.408414 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.1741 (* 0.0454545 = 0.144277 loss) | |
| I0404 14:35:41.408438 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.24782 (* 0.0454545 = 0.147628 loss) | |
| I0404 14:35:41.408454 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.36382 (* 0.0454545 = 0.152901 loss) | |
| I0404 14:35:41.408468 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.7093 (* 0.0454545 = 0.12315 loss) | |
| I0404 14:35:41.408483 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.22581 (* 0.0454545 = 0.101173 loss) | |
| I0404 14:35:41.408495 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.33817 (* 0.0454545 = 0.0608257 loss) | |
| I0404 14:35:41.408509 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.347255 (* 0.0454545 = 0.0157843 loss) | |
| I0404 14:35:41.408524 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.121954 (* 0.0454545 = 0.00554335 loss) | |
| I0404 14:35:41.408537 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.128497 (* 0.0454545 = 0.00584077 loss) | |
| I0404 14:35:41.408555 9252 solver.cpp:245] Train net output #32: loss/loss11 = 8.26545e-05 (* 0.0454545 = 3.75702e-06 loss) | |
| I0404 14:35:41.408571 9252 solver.cpp:245] Train net output #33: loss/loss12 = 9.08421e-05 (* 0.0454545 = 4.12919e-06 loss) | |
| I0404 14:35:41.408584 9252 solver.cpp:245] Train net output #34: loss/loss13 = 8.72765e-05 (* 0.0454545 = 3.96711e-06 loss) | |
| I0404 14:35:41.408598 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.81413e-05 (* 0.0454545 = 3.55188e-06 loss) | |
| I0404 14:35:41.408612 9252 solver.cpp:245] Train net output #36: loss/loss15 = 8.38291e-05 (* 0.0454545 = 3.81042e-06 loss) | |
| I0404 14:35:41.408627 9252 solver.cpp:245] Train net output #37: loss/loss16 = 7.15972e-05 (* 0.0454545 = 3.25442e-06 loss) | |
| I0404 14:35:41.408648 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.11835e-05 (* 0.0454545 = 3.23562e-06 loss) | |
| I0404 14:35:41.408677 9252 solver.cpp:245] Train net output #39: loss/loss18 = 8.60404e-05 (* 0.0454545 = 3.91093e-06 loss) | |
| I0404 14:35:41.408692 9252 solver.cpp:245] Train net output #40: loss/loss19 = 7.21477e-05 (* 0.0454545 = 3.27944e-06 loss) | |
| I0404 14:35:41.408706 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.65956e-05 (* 0.0454545 = 3.48162e-06 loss) | |
| I0404 14:35:41.408720 9252 solver.cpp:245] Train net output #42: loss/loss21 = 8.04684e-05 (* 0.0454545 = 3.65765e-06 loss) | |
| I0404 14:35:41.408735 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.49242e-05 (* 0.0454545 = 3.40565e-06 loss) | |
| I0404 14:35:41.408749 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:35:41.408761 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000285589 | |
| I0404 14:35:41.408776 9252 sgd_solver.cpp:106] Iteration 45000, lr = 0.00955 | |
| I0404 14:36:52.520340 9252 solver.cpp:229] Iteration 45500, loss = 0.87689 | |
| I0404 14:36:52.520480 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 14:36:52.520519 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:36:52.520542 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.1875 | |
| I0404 14:36:52.520565 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 14:36:52.520588 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 14:36:52.520609 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 14:36:52.520632 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 14:36:52.520653 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:36:52.520678 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:36:52.520701 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:36:52.520722 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:36:52.520747 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:36:52.520771 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:36:52.520799 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:36:52.520823 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:36:52.520843 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:36:52.520864 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:36:52.520884 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:36:52.520905 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:36:52.520926 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:36:52.520947 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:36:52.520968 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:36:52.520994 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.65098 (* 0.0454545 = 0.120499 loss) | |
| I0404 14:36:52.521020 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.02274 (* 0.0454545 = 0.137397 loss) | |
| I0404 14:36:52.521054 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.1615 (* 0.0454545 = 0.143705 loss) | |
| I0404 14:36:52.521080 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.99287 (* 0.0454545 = 0.136039 loss) | |
| I0404 14:36:52.521113 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.6586 (* 0.0454545 = 0.120846 loss) | |
| I0404 14:36:52.521138 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.99495 (* 0.0454545 = 0.0906795 loss) | |
| I0404 14:36:52.521172 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.27414 (* 0.0454545 = 0.0579155 loss) | |
| I0404 14:36:52.521198 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.253435 (* 0.0454545 = 0.0115198 loss) | |
| I0404 14:36:52.521226 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.278649 (* 0.0454545 = 0.0126659 loss) | |
| I0404 14:36:52.521257 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00618084 (* 0.0454545 = 0.000280947 loss) | |
| I0404 14:36:52.521286 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.44787e-05 (* 0.0454545 = 1.56721e-06 loss) | |
| I0404 14:36:52.521312 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.08241e-05 (* 0.0454545 = 1.40109e-06 loss) | |
| I0404 14:36:52.521338 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.14461e-05 (* 0.0454545 = 1.42937e-06 loss) | |
| I0404 14:36:52.521373 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.21652e-05 (* 0.0454545 = 1.46205e-06 loss) | |
| I0404 14:36:52.521399 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.38342e-05 (* 0.0454545 = 1.53792e-06 loss) | |
| I0404 14:36:52.521445 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.36181e-05 (* 0.0454545 = 1.5281e-06 loss) | |
| I0404 14:36:52.521476 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.28431e-05 (* 0.0454545 = 1.49287e-06 loss) | |
| I0404 14:36:52.521538 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.95424e-05 (* 0.0454545 = 1.34284e-06 loss) | |
| I0404 14:36:52.521567 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.06973e-05 (* 0.0454545 = 1.39533e-06 loss) | |
| I0404 14:36:52.521594 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.26085e-05 (* 0.0454545 = 1.4822e-06 loss) | |
| I0404 14:36:52.521620 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.95163e-05 (* 0.0454545 = 1.34165e-06 loss) | |
| I0404 14:36:52.521646 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.12748e-05 (* 0.0454545 = 1.42158e-06 loss) | |
| I0404 14:36:52.521669 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:36:52.521690 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000246901 | |
| I0404 14:36:52.521714 9252 sgd_solver.cpp:106] Iteration 45500, lr = 0.009545 | |
| I0404 14:38:03.553040 9252 solver.cpp:229] Iteration 46000, loss = 0.87465 | |
| I0404 14:38:03.553135 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.28125 | |
| I0404 14:38:03.553164 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:38:03.553192 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 14:38:03.553215 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 14:38:03.553236 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 14:38:03.553257 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.28125 | |
| I0404 14:38:03.553279 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 14:38:03.553302 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:38:03.553325 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:38:03.553349 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:38:03.553370 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:38:03.553391 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:38:03.553448 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:38:03.553484 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:38:03.553506 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:38:03.553529 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:38:03.553550 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:38:03.553570 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:38:03.553591 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:38:03.553613 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:38:03.553633 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:38:03.553655 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:38:03.553683 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.6315 (* 0.0454545 = 0.119614 loss) | |
| I0404 14:38:03.553709 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.11341 (* 0.0454545 = 0.141519 loss) | |
| I0404 14:38:03.553735 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.11689 (* 0.0454545 = 0.141677 loss) | |
| I0404 14:38:03.553761 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.36347 (* 0.0454545 = 0.152885 loss) | |
| I0404 14:38:03.553786 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.83869 (* 0.0454545 = 0.129031 loss) | |
| I0404 14:38:03.553813 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.65447 (* 0.0454545 = 0.120658 loss) | |
| I0404 14:38:03.553838 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.54381 (* 0.0454545 = 0.0701732 loss) | |
| I0404 14:38:03.553864 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.50787 (* 0.0454545 = 0.023085 loss) | |
| I0404 14:38:03.553889 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.131092 (* 0.0454545 = 0.00595874 loss) | |
| I0404 14:38:03.553921 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.156247 (* 0.0454545 = 0.00710212 loss) | |
| I0404 14:38:03.553953 9252 solver.cpp:245] Train net output #32: loss/loss11 = 8.85845e-05 (* 0.0454545 = 4.02657e-06 loss) | |
| I0404 14:38:03.553980 9252 solver.cpp:245] Train net output #33: loss/loss12 = 8.1286e-05 (* 0.0454545 = 3.69482e-06 loss) | |
| I0404 14:38:03.554014 9252 solver.cpp:245] Train net output #34: loss/loss13 = 8.42095e-05 (* 0.0454545 = 3.82771e-06 loss) | |
| I0404 14:38:03.554041 9252 solver.cpp:245] Train net output #35: loss/loss14 = 8.08844e-05 (* 0.0454545 = 3.67657e-06 loss) | |
| I0404 14:38:03.554074 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.74461e-05 (* 0.0454545 = 3.52028e-06 loss) | |
| I0404 14:38:03.554100 9252 solver.cpp:245] Train net output #37: loss/loss16 = 9.20744e-05 (* 0.0454545 = 4.1852e-06 loss) | |
| I0404 14:38:03.554124 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.98623e-05 (* 0.0454545 = 3.6301e-06 loss) | |
| I0404 14:38:03.554174 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.44442e-05 (* 0.0454545 = 3.38383e-06 loss) | |
| I0404 14:38:03.554201 9252 solver.cpp:245] Train net output #40: loss/loss19 = 7.22845e-05 (* 0.0454545 = 3.28566e-06 loss) | |
| I0404 14:38:03.554229 9252 solver.cpp:245] Train net output #41: loss/loss20 = 8.21616e-05 (* 0.0454545 = 3.73462e-06 loss) | |
| I0404 14:38:03.554253 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.84345e-05 (* 0.0454545 = 3.5652e-06 loss) | |
| I0404 14:38:03.554280 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.94919e-05 (* 0.0454545 = 3.61327e-06 loss) | |
| I0404 14:38:03.554301 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:38:03.554323 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000134733 | |
| I0404 14:38:03.554347 9252 sgd_solver.cpp:106] Iteration 46000, lr = 0.00954 | |
| I0404 14:39:14.461668 9252 solver.cpp:229] Iteration 46500, loss = 0.875108 | |
| I0404 14:39:14.461798 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 14:39:14.461818 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:39:14.461832 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:39:14.461843 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 14:39:14.461855 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 14:39:14.461869 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 14:39:14.461881 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 14:39:14.461894 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 14:39:14.461905 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:39:14.461916 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:39:14.461928 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:39:14.461941 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:39:14.461951 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:39:14.461963 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:39:14.461974 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:39:14.461985 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:39:14.461997 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:39:14.462009 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:39:14.462020 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:39:14.462033 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:39:14.462044 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:39:14.462054 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:39:14.462070 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.92269 (* 0.0454545 = 0.13285 loss) | |
| I0404 14:39:14.462085 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.27663 (* 0.0454545 = 0.148938 loss) | |
| I0404 14:39:14.462098 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.21398 (* 0.0454545 = 0.14609 loss) | |
| I0404 14:39:14.462112 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.25238 (* 0.0454545 = 0.147836 loss) | |
| I0404 14:39:14.462126 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.67537 (* 0.0454545 = 0.121608 loss) | |
| I0404 14:39:14.462139 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.26181 (* 0.0454545 = 0.10281 loss) | |
| I0404 14:39:14.462152 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.960737 (* 0.0454545 = 0.0436699 loss) | |
| I0404 14:39:14.462167 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.1945 (* 0.0454545 = 0.00884092 loss) | |
| I0404 14:39:14.462180 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.158181 (* 0.0454545 = 0.00719005 loss) | |
| I0404 14:39:14.462194 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0065497 (* 0.0454545 = 0.000297714 loss) | |
| I0404 14:39:14.462208 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.33793e-05 (* 0.0454545 = 2.88088e-06 loss) | |
| I0404 14:39:14.462223 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.26986e-05 (* 0.0454545 = 3.30448e-06 loss) | |
| I0404 14:39:14.462236 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.40293e-05 (* 0.0454545 = 2.91042e-06 loss) | |
| I0404 14:39:14.462250 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.72476e-05 (* 0.0454545 = 3.05671e-06 loss) | |
| I0404 14:39:14.462265 9252 solver.cpp:245] Train net output #36: loss/loss15 = 6.37703e-05 (* 0.0454545 = 2.89865e-06 loss) | |
| I0404 14:39:14.462278 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.74666e-05 (* 0.0454545 = 3.06666e-06 loss) | |
| I0404 14:39:14.462292 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.98737e-05 (* 0.0454545 = 2.72153e-06 loss) | |
| I0404 14:39:14.462323 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.48898e-05 (* 0.0454545 = 2.49499e-06 loss) | |
| I0404 14:39:14.462338 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.17597e-05 (* 0.0454545 = 2.80726e-06 loss) | |
| I0404 14:39:14.462352 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.78909e-05 (* 0.0454545 = 2.6314e-06 loss) | |
| I0404 14:39:14.462366 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.10444e-05 (* 0.0454545 = 2.77474e-06 loss) | |
| I0404 14:39:14.462379 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.11112e-05 (* 0.0454545 = 2.77778e-06 loss) | |
| I0404 14:39:14.462391 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:39:14.462404 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000606647 | |
| I0404 14:39:14.462417 9252 sgd_solver.cpp:106] Iteration 46500, lr = 0.009535 | |
| I0404 14:40:25.318440 9252 solver.cpp:229] Iteration 47000, loss = 0.874887 | |
| I0404 14:40:25.318616 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.15625 | |
| I0404 14:40:25.318637 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 14:40:25.318650 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 14:40:25.318663 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 14:40:25.318675 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 14:40:25.318687 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 14:40:25.318699 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 14:40:25.318711 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 14:40:25.318723 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:40:25.318735 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 14:40:25.318749 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:40:25.318763 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:40:25.318773 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:40:25.318785 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:40:25.318797 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:40:25.318809 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:40:25.318820 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:40:25.318832 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:40:25.318843 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:40:25.318856 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:40:25.318867 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:40:25.318878 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:40:25.318894 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.78944 (* 0.0454545 = 0.126793 loss) | |
| I0404 14:40:25.318908 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.29474 (* 0.0454545 = 0.149761 loss) | |
| I0404 14:40:25.318922 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.2549 (* 0.0454545 = 0.14795 loss) | |
| I0404 14:40:25.318936 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.25335 (* 0.0454545 = 0.147879 loss) | |
| I0404 14:40:25.318950 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.95724 (* 0.0454545 = 0.13442 loss) | |
| I0404 14:40:25.318964 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.42056 (* 0.0454545 = 0.110025 loss) | |
| I0404 14:40:25.318977 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.18674 (* 0.0454545 = 0.0539429 loss) | |
| I0404 14:40:25.318991 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.477054 (* 0.0454545 = 0.0216843 loss) | |
| I0404 14:40:25.319005 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.275115 (* 0.0454545 = 0.0125052 loss) | |
| I0404 14:40:25.319018 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.27526 (* 0.0454545 = 0.0125118 loss) | |
| I0404 14:40:25.319032 9252 solver.cpp:245] Train net output #32: loss/loss11 = 5.52418e-05 (* 0.0454545 = 2.51099e-06 loss) | |
| I0404 14:40:25.319046 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.85006e-05 (* 0.0454545 = 2.20457e-06 loss) | |
| I0404 14:40:25.319061 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.9469e-05 (* 0.0454545 = 2.24859e-06 loss) | |
| I0404 14:40:25.319075 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.48743e-05 (* 0.0454545 = 2.49429e-06 loss) | |
| I0404 14:40:25.319089 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.54029e-05 (* 0.0454545 = 2.06377e-06 loss) | |
| I0404 14:40:25.319103 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.16791e-05 (* 0.0454545 = 2.34905e-06 loss) | |
| I0404 14:40:25.319118 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.47638e-05 (* 0.0454545 = 2.03472e-06 loss) | |
| I0404 14:40:25.319144 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.00273e-05 (* 0.0454545 = 2.27397e-06 loss) | |
| I0404 14:40:25.319159 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.80334e-05 (* 0.0454545 = 2.18334e-06 loss) | |
| I0404 14:40:25.319174 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.71393e-05 (* 0.0454545 = 2.1427e-06 loss) | |
| I0404 14:40:25.319187 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.53165e-05 (* 0.0454545 = 2.05984e-06 loss) | |
| I0404 14:40:25.319201 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.37942e-05 (* 0.0454545 = 1.99065e-06 loss) | |
| I0404 14:40:25.319213 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:40:25.319226 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000144815 | |
| I0404 14:40:25.319239 9252 sgd_solver.cpp:106] Iteration 47000, lr = 0.00953 | |
| I0404 14:41:36.640952 9252 solver.cpp:229] Iteration 47500, loss = 0.871602 | |
| I0404 14:41:36.641062 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.34375 | |
| I0404 14:41:36.641080 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 14:41:36.641093 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 14:41:36.641105 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 14:41:36.641119 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 14:41:36.641130 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 14:41:36.641142 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 14:41:36.641155 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:41:36.641165 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:41:36.641178 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:41:36.641190 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:41:36.641201 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:41:36.641212 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:41:36.641224 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:41:36.641235 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:41:36.641247 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:41:36.641261 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:41:36.641273 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:41:36.641284 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:41:36.641296 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:41:36.641316 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:41:36.641329 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:41:36.641343 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.60972 (* 0.0454545 = 0.118624 loss) | |
| I0404 14:41:36.641357 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.1853 (* 0.0454545 = 0.144786 loss) | |
| I0404 14:41:36.641371 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.39465 (* 0.0454545 = 0.154302 loss) | |
| I0404 14:41:36.641386 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.82761 (* 0.0454545 = 0.128528 loss) | |
| I0404 14:41:36.641399 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.39648 (* 0.0454545 = 0.108931 loss) | |
| I0404 14:41:36.641413 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.88806 (* 0.0454545 = 0.0858211 loss) | |
| I0404 14:41:36.641451 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.16534 (* 0.0454545 = 0.0529699 loss) | |
| I0404 14:41:36.641466 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.394399 (* 0.0454545 = 0.0179272 loss) | |
| I0404 14:41:36.641480 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.125138 (* 0.0454545 = 0.00568807 loss) | |
| I0404 14:41:36.641494 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00454959 (* 0.0454545 = 0.0002068 loss) | |
| I0404 14:41:36.641510 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.50208e-05 (* 0.0454545 = 6.82765e-07 loss) | |
| I0404 14:41:36.641525 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.381e-05 (* 0.0454545 = 6.27728e-07 loss) | |
| I0404 14:41:36.641538 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.19845e-05 (* 0.0454545 = 5.4475e-07 loss) | |
| I0404 14:41:36.641551 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.43652e-05 (* 0.0454545 = 6.52963e-07 loss) | |
| I0404 14:41:36.641566 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.31208e-05 (* 0.0454545 = 5.96401e-07 loss) | |
| I0404 14:41:36.641579 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.50432e-05 (* 0.0454545 = 6.83781e-07 loss) | |
| I0404 14:41:36.641592 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.27408e-05 (* 0.0454545 = 5.79128e-07 loss) | |
| I0404 14:41:36.641625 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.096e-05 (* 0.0454545 = 4.98182e-07 loss) | |
| I0404 14:41:36.641640 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.2942e-05 (* 0.0454545 = 5.88273e-07 loss) | |
| I0404 14:41:36.641654 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.30687e-05 (* 0.0454545 = 5.9403e-07 loss) | |
| I0404 14:41:36.641669 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.21112e-05 (* 0.0454545 = 5.50509e-07 loss) | |
| I0404 14:41:36.641682 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.17386e-05 (* 0.0454545 = 5.33575e-07 loss) | |
| I0404 14:41:36.641695 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:41:36.641705 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00047497 | |
| I0404 14:41:36.641719 9252 sgd_solver.cpp:106] Iteration 47500, lr = 0.009525 | |
| I0404 14:42:47.657389 9252 solver.cpp:229] Iteration 48000, loss = 0.870502 | |
| I0404 14:42:47.657531 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.3125 | |
| I0404 14:42:47.657551 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 14:42:47.657563 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 14:42:47.657577 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 14:42:47.657588 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 14:42:47.657600 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 14:42:47.657611 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 14:42:47.657624 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:42:47.657635 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 14:42:47.657647 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:42:47.657660 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:42:47.657670 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:42:47.657682 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:42:47.657696 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:42:47.657706 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:42:47.657718 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:42:47.657729 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:42:47.657740 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:42:47.657758 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:42:47.657770 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:42:47.657783 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:42:47.657793 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:42:47.657809 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.63326 (* 0.0454545 = 0.119694 loss) | |
| I0404 14:42:47.657824 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.1412 (* 0.0454545 = 0.142782 loss) | |
| I0404 14:42:47.657845 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.24709 (* 0.0454545 = 0.147595 loss) | |
| I0404 14:42:47.657858 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.32539 (* 0.0454545 = 0.151154 loss) | |
| I0404 14:42:47.657872 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.64822 (* 0.0454545 = 0.120373 loss) | |
| I0404 14:42:47.657886 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.96504 (* 0.0454545 = 0.0893199 loss) | |
| I0404 14:42:47.657899 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.09165 (* 0.0454545 = 0.0496205 loss) | |
| I0404 14:42:47.657913 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.685885 (* 0.0454545 = 0.0311766 loss) | |
| I0404 14:42:47.657927 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.442841 (* 0.0454545 = 0.0201291 loss) | |
| I0404 14:42:47.657940 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0120296 (* 0.0454545 = 0.000546801 loss) | |
| I0404 14:42:47.657955 9252 solver.cpp:245] Train net output #32: loss/loss11 = 8.57489e-05 (* 0.0454545 = 3.89768e-06 loss) | |
| I0404 14:42:47.657969 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.69526e-05 (* 0.0454545 = 3.49784e-06 loss) | |
| I0404 14:42:47.657984 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.84612e-05 (* 0.0454545 = 3.56642e-06 loss) | |
| I0404 14:42:47.657997 9252 solver.cpp:245] Train net output #35: loss/loss14 = 8.34264e-05 (* 0.0454545 = 3.79211e-06 loss) | |
| I0404 14:42:47.658011 9252 solver.cpp:245] Train net output #36: loss/loss15 = 8.2842e-05 (* 0.0454545 = 3.76555e-06 loss) | |
| I0404 14:42:47.658025 9252 solver.cpp:245] Train net output #37: loss/loss16 = 8.62815e-05 (* 0.0454545 = 3.92188e-06 loss) | |
| I0404 14:42:47.658040 9252 solver.cpp:245] Train net output #38: loss/loss17 = 8.05583e-05 (* 0.0454545 = 3.66174e-06 loss) | |
| I0404 14:42:47.658071 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.80823e-05 (* 0.0454545 = 3.5492e-06 loss) | |
| I0404 14:42:47.658092 9252 solver.cpp:245] Train net output #40: loss/loss19 = 8.08729e-05 (* 0.0454545 = 3.67604e-06 loss) | |
| I0404 14:42:47.658105 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.99378e-05 (* 0.0454545 = 3.63354e-06 loss) | |
| I0404 14:42:47.658119 9252 solver.cpp:245] Train net output #42: loss/loss21 = 8.37496e-05 (* 0.0454545 = 3.8068e-06 loss) | |
| I0404 14:42:47.658133 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.80348e-05 (* 0.0454545 = 3.54704e-06 loss) | |
| I0404 14:42:47.658150 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:42:47.658162 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000132831 | |
| I0404 14:42:47.658176 9252 sgd_solver.cpp:106] Iteration 48000, lr = 0.00952 | |
| I0404 14:43:58.677539 9252 solver.cpp:229] Iteration 48500, loss = 0.871438 | |
| I0404 14:43:58.677667 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.28125 | |
| I0404 14:43:58.677686 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:43:58.677700 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.1875 | |
| I0404 14:43:58.677713 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 14:43:58.677726 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 14:43:58.677737 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 14:43:58.677752 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.59375 | |
| I0404 14:43:58.677763 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:43:58.677775 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:43:58.677788 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 14:43:58.677799 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:43:58.677811 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:43:58.677822 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:43:58.677834 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:43:58.677845 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:43:58.677857 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:43:58.677870 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:43:58.677881 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:43:58.677891 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:43:58.677903 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:43:58.677923 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:43:58.677934 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:43:58.677949 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.53376 (* 0.0454545 = 0.115171 loss) | |
| I0404 14:43:58.677964 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.005 (* 0.0454545 = 0.136591 loss) | |
| I0404 14:43:58.677978 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.04007 (* 0.0454545 = 0.138185 loss) | |
| I0404 14:43:58.677999 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.88366 (* 0.0454545 = 0.131075 loss) | |
| I0404 14:43:58.678012 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.5697 (* 0.0454545 = 0.116804 loss) | |
| I0404 14:43:58.678026 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.31008 (* 0.0454545 = 0.105003 loss) | |
| I0404 14:43:58.678040 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.51197 (* 0.0454545 = 0.0687259 loss) | |
| I0404 14:43:58.678053 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.278279 (* 0.0454545 = 0.012649 loss) | |
| I0404 14:43:58.678067 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.310078 (* 0.0454545 = 0.0140945 loss) | |
| I0404 14:43:58.678081 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.370345 (* 0.0454545 = 0.0168339 loss) | |
| I0404 14:43:58.678095 9252 solver.cpp:245] Train net output #32: loss/loss11 = 9.28396e-05 (* 0.0454545 = 4.21998e-06 loss) | |
| I0404 14:43:58.678109 9252 solver.cpp:245] Train net output #33: loss/loss12 = 8.8484e-05 (* 0.0454545 = 4.022e-06 loss) | |
| I0404 14:43:58.678123 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.67631e-05 (* 0.0454545 = 3.48923e-06 loss) | |
| I0404 14:43:58.678138 9252 solver.cpp:245] Train net output #35: loss/loss14 = 9.0974e-05 (* 0.0454545 = 4.13518e-06 loss) | |
| I0404 14:43:58.678151 9252 solver.cpp:245] Train net output #36: loss/loss15 = 8.82745e-05 (* 0.0454545 = 4.01248e-06 loss) | |
| I0404 14:43:58.678165 9252 solver.cpp:245] Train net output #37: loss/loss16 = 9.13974e-05 (* 0.0454545 = 4.15443e-06 loss) | |
| I0404 14:43:58.678179 9252 solver.cpp:245] Train net output #38: loss/loss17 = 8.33841e-05 (* 0.0454545 = 3.79019e-06 loss) | |
| I0404 14:43:58.678210 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.89866e-05 (* 0.0454545 = 3.5903e-06 loss) | |
| I0404 14:43:58.678225 9252 solver.cpp:245] Train net output #40: loss/loss19 = 8.32478e-05 (* 0.0454545 = 3.78399e-06 loss) | |
| I0404 14:43:58.678239 9252 solver.cpp:245] Train net output #41: loss/loss20 = 8.92621e-05 (* 0.0454545 = 4.05737e-06 loss) | |
| I0404 14:43:58.678253 9252 solver.cpp:245] Train net output #42: loss/loss21 = 8.06727e-05 (* 0.0454545 = 3.66694e-06 loss) | |
| I0404 14:43:58.678267 9252 solver.cpp:245] Train net output #43: loss/loss22 = 8.30279e-05 (* 0.0454545 = 3.77399e-06 loss) | |
| I0404 14:43:58.678278 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:43:58.678289 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000301717 | |
| I0404 14:43:58.678303 9252 sgd_solver.cpp:106] Iteration 48500, lr = 0.009515 | |
| I0404 14:45:09.874650 9252 solver.cpp:229] Iteration 49000, loss = 0.866828 | |
| I0404 14:45:09.874874 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 14:45:09.874896 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:45:09.874909 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 14:45:09.874922 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 14:45:09.874933 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 14:45:09.874945 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 14:45:09.874958 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 14:45:09.874969 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:45:09.874981 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 14:45:09.874994 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:45:09.875005 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:45:09.875016 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:45:09.875027 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:45:09.875039 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:45:09.875051 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:45:09.875062 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:45:09.875074 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:45:09.875085 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:45:09.875097 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:45:09.875109 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:45:09.875120 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:45:09.875133 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:45:09.875149 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.93127 (* 0.0454545 = 0.13324 loss) | |
| I0404 14:45:09.875162 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.06717 (* 0.0454545 = 0.139417 loss) | |
| I0404 14:45:09.875176 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.23679 (* 0.0454545 = 0.147127 loss) | |
| I0404 14:45:09.875190 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.16083 (* 0.0454545 = 0.143674 loss) | |
| I0404 14:45:09.875203 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.89202 (* 0.0454545 = 0.131456 loss) | |
| I0404 14:45:09.875217 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.97476 (* 0.0454545 = 0.135216 loss) | |
| I0404 14:45:09.875231 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.07561 (* 0.0454545 = 0.0488912 loss) | |
| I0404 14:45:09.875244 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.334123 (* 0.0454545 = 0.0151874 loss) | |
| I0404 14:45:09.875258 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0374249 (* 0.0454545 = 0.00170113 loss) | |
| I0404 14:45:09.875272 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00960966 (* 0.0454545 = 0.000436803 loss) | |
| I0404 14:45:09.875290 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000128528 (* 0.0454545 = 5.84216e-06 loss) | |
| I0404 14:45:09.875304 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000108776 (* 0.0454545 = 4.94436e-06 loss) | |
| I0404 14:45:09.875319 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000101019 (* 0.0454545 = 4.59175e-06 loss) | |
| I0404 14:45:09.875332 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000112651 (* 0.0454545 = 5.12049e-06 loss) | |
| I0404 14:45:09.875352 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000106099 (* 0.0454545 = 4.82268e-06 loss) | |
| I0404 14:45:09.875370 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000118558 (* 0.0454545 = 5.38901e-06 loss) | |
| I0404 14:45:09.875385 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000108465 (* 0.0454545 = 4.93022e-06 loss) | |
| I0404 14:45:09.875412 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000113035 (* 0.0454545 = 5.13798e-06 loss) | |
| I0404 14:45:09.875427 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000113091 (* 0.0454545 = 5.14048e-06 loss) | |
| I0404 14:45:09.875442 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000106853 (* 0.0454545 = 4.85694e-06 loss) | |
| I0404 14:45:09.875455 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.000107757 (* 0.0454545 = 4.89804e-06 loss) | |
| I0404 14:45:09.875469 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000107387 (* 0.0454545 = 4.88123e-06 loss) | |
| I0404 14:45:09.875481 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:45:09.875493 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000154032 | |
| I0404 14:45:09.875506 9252 sgd_solver.cpp:106] Iteration 49000, lr = 0.00951 | |
| I0404 14:46:21.435170 9252 solver.cpp:229] Iteration 49500, loss = 0.862061 | |
| I0404 14:46:21.435356 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 14:46:21.435374 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:46:21.435387 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 14:46:21.435400 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 14:46:21.435411 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 14:46:21.435423 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 14:46:21.435434 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.875 | |
| I0404 14:46:21.435446 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 14:46:21.435458 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:46:21.435470 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:46:21.435482 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:46:21.435494 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:46:21.435505 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:46:21.435518 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:46:21.435528 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:46:21.435540 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:46:21.435551 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:46:21.435562 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:46:21.435575 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:46:21.435585 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:46:21.435597 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:46:21.435616 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:46:21.435631 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.60703 (* 0.0454545 = 0.118501 loss) | |
| I0404 14:46:21.435645 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.00991 (* 0.0454545 = 0.136814 loss) | |
| I0404 14:46:21.435659 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.90489 (* 0.0454545 = 0.132041 loss) | |
| I0404 14:46:21.435673 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.95529 (* 0.0454545 = 0.134331 loss) | |
| I0404 14:46:21.435688 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.46965 (* 0.0454545 = 0.112257 loss) | |
| I0404 14:46:21.435703 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.39203 (* 0.0454545 = 0.108729 loss) | |
| I0404 14:46:21.435715 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.612338 (* 0.0454545 = 0.0278335 loss) | |
| I0404 14:46:21.435729 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.114317 (* 0.0454545 = 0.00519624 loss) | |
| I0404 14:46:21.435744 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.124568 (* 0.0454545 = 0.00566219 loss) | |
| I0404 14:46:21.435757 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.117222 (* 0.0454545 = 0.00532829 loss) | |
| I0404 14:46:21.435771 9252 solver.cpp:245] Train net output #32: loss/loss11 = 5.76242e-05 (* 0.0454545 = 2.61928e-06 loss) | |
| I0404 14:46:21.435786 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.73632e-05 (* 0.0454545 = 2.60742e-06 loss) | |
| I0404 14:46:21.435799 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.28416e-05 (* 0.0454545 = 2.40189e-06 loss) | |
| I0404 14:46:21.435813 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.78388e-05 (* 0.0454545 = 2.62904e-06 loss) | |
| I0404 14:46:21.435827 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.45397e-05 (* 0.0454545 = 2.47908e-06 loss) | |
| I0404 14:46:21.435840 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.68678e-05 (* 0.0454545 = 3.03945e-06 loss) | |
| I0404 14:46:21.435854 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.30183e-05 (* 0.0454545 = 2.40992e-06 loss) | |
| I0404 14:46:21.435884 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.98353e-05 (* 0.0454545 = 2.26524e-06 loss) | |
| I0404 14:46:21.435904 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.8355e-05 (* 0.0454545 = 2.6525e-06 loss) | |
| I0404 14:46:21.435919 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.69998e-05 (* 0.0454545 = 2.5909e-06 loss) | |
| I0404 14:46:21.435933 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.29523e-05 (* 0.0454545 = 2.40692e-06 loss) | |
| I0404 14:46:21.435946 9252 solver.cpp:245] Train net output #43: loss/loss22 = 5.29369e-05 (* 0.0454545 = 2.40622e-06 loss) | |
| I0404 14:46:21.435958 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:46:21.435971 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00167971 | |
| I0404 14:46:21.435987 9252 sgd_solver.cpp:106] Iteration 49500, lr = 0.009505 | |
| I0404 14:47:32.431826 9252 solver.cpp:338] Iteration 50000, Testing net (#0) | |
| I0404 14:47:40.460253 9252 solver.cpp:393] Test loss: 0.752377 | |
| I0404 14:47:40.460302 9252 solver.cpp:406] Test net output #0: loss/accuracy01 = 0.259 | |
| I0404 14:47:40.460319 9252 solver.cpp:406] Test net output #1: loss/accuracy02 = 0.136 | |
| I0404 14:47:40.460330 9252 solver.cpp:406] Test net output #2: loss/accuracy03 = 0.116 | |
| I0404 14:47:40.460342 9252 solver.cpp:406] Test net output #3: loss/accuracy04 = 0.159 | |
| I0404 14:47:40.460355 9252 solver.cpp:406] Test net output #4: loss/accuracy05 = 0.279 | |
| I0404 14:47:40.460366 9252 solver.cpp:406] Test net output #5: loss/accuracy06 = 0.508 | |
| I0404 14:47:40.460377 9252 solver.cpp:406] Test net output #6: loss/accuracy07 = 0.892 | |
| I0404 14:47:40.460388 9252 solver.cpp:406] Test net output #7: loss/accuracy08 = 0.97 | |
| I0404 14:47:40.460399 9252 solver.cpp:406] Test net output #8: loss/accuracy09 = 0.995 | |
| I0404 14:47:40.460410 9252 solver.cpp:406] Test net output #9: loss/accuracy10 = 0.998 | |
| I0404 14:47:40.460422 9252 solver.cpp:406] Test net output #10: loss/accuracy11 = 1 | |
| I0404 14:47:40.460433 9252 solver.cpp:406] Test net output #11: loss/accuracy12 = 1 | |
| I0404 14:47:40.460444 9252 solver.cpp:406] Test net output #12: loss/accuracy13 = 1 | |
| I0404 14:47:40.460455 9252 solver.cpp:406] Test net output #13: loss/accuracy14 = 1 | |
| I0404 14:47:40.460466 9252 solver.cpp:406] Test net output #14: loss/accuracy15 = 1 | |
| I0404 14:47:40.460477 9252 solver.cpp:406] Test net output #15: loss/accuracy16 = 1 | |
| I0404 14:47:40.460489 9252 solver.cpp:406] Test net output #16: loss/accuracy17 = 1 | |
| I0404 14:47:40.460500 9252 solver.cpp:406] Test net output #17: loss/accuracy18 = 1 | |
| I0404 14:47:40.460511 9252 solver.cpp:406] Test net output #18: loss/accuracy19 = 1 | |
| I0404 14:47:40.460521 9252 solver.cpp:406] Test net output #19: loss/accuracy20 = 1 | |
| I0404 14:47:40.460532 9252 solver.cpp:406] Test net output #20: loss/accuracy21 = 1 | |
| I0404 14:47:40.460543 9252 solver.cpp:406] Test net output #21: loss/accuracy22 = 1 | |
| I0404 14:47:40.460558 9252 solver.cpp:406] Test net output #22: loss/loss01 = 2.49573 (* 0.0454545 = 0.113442 loss) | |
| I0404 14:47:40.460572 9252 solver.cpp:406] Test net output #23: loss/loss02 = 2.86604 (* 0.0454545 = 0.130275 loss) | |
| I0404 14:47:40.460587 9252 solver.cpp:406] Test net output #24: loss/loss03 = 2.98331 (* 0.0454545 = 0.135605 loss) | |
| I0404 14:47:40.460600 9252 solver.cpp:406] Test net output #25: loss/loss04 = 2.92324 (* 0.0454545 = 0.132875 loss) | |
| I0404 14:47:40.460613 9252 solver.cpp:406] Test net output #26: loss/loss05 = 2.60669 (* 0.0454545 = 0.118486 loss) | |
| I0404 14:47:40.460628 9252 solver.cpp:406] Test net output #27: loss/loss06 = 1.89308 (* 0.0454545 = 0.0860492 loss) | |
| I0404 14:47:40.460640 9252 solver.cpp:406] Test net output #28: loss/loss07 = 0.52004 (* 0.0454545 = 0.0236382 loss) | |
| I0404 14:47:40.460654 9252 solver.cpp:406] Test net output #29: loss/loss08 = 0.18919 (* 0.0454545 = 0.00859953 loss) | |
| I0404 14:47:40.460667 9252 solver.cpp:406] Test net output #30: loss/loss09 = 0.0484217 (* 0.0454545 = 0.00220098 loss) | |
| I0404 14:47:40.460681 9252 solver.cpp:406] Test net output #31: loss/loss10 = 0.0255867 (* 0.0454545 = 0.00116303 loss) | |
| I0404 14:47:40.460695 9252 solver.cpp:406] Test net output #32: loss/loss11 = 8.33588e-05 (* 0.0454545 = 3.78904e-06 loss) | |
| I0404 14:47:40.460708 9252 solver.cpp:406] Test net output #33: loss/loss12 = 7.79565e-05 (* 0.0454545 = 3.54348e-06 loss) | |
| I0404 14:47:40.460722 9252 solver.cpp:406] Test net output #34: loss/loss13 = 7.21491e-05 (* 0.0454545 = 3.27951e-06 loss) | |
| I0404 14:47:40.460736 9252 solver.cpp:406] Test net output #35: loss/loss14 = 8.07223e-05 (* 0.0454545 = 3.66919e-06 loss) | |
| I0404 14:47:40.460753 9252 solver.cpp:406] Test net output #36: loss/loss15 = 7.53565e-05 (* 0.0454545 = 3.4253e-06 loss) | |
| I0404 14:47:40.460767 9252 solver.cpp:406] Test net output #37: loss/loss16 = 8.64314e-05 (* 0.0454545 = 3.9287e-06 loss) | |
| I0404 14:47:40.460780 9252 solver.cpp:406] Test net output #38: loss/loss17 = 7.53364e-05 (* 0.0454545 = 3.42438e-06 loss) | |
| I0404 14:47:40.460830 9252 solver.cpp:406] Test net output #39: loss/loss18 = 7.21894e-05 (* 0.0454545 = 3.28134e-06 loss) | |
| I0404 14:47:40.460845 9252 solver.cpp:406] Test net output #40: loss/loss19 = 8.11979e-05 (* 0.0454545 = 3.69081e-06 loss) | |
| I0404 14:47:40.460860 9252 solver.cpp:406] Test net output #41: loss/loss20 = 7.90006e-05 (* 0.0454545 = 3.59093e-06 loss) | |
| I0404 14:47:40.460872 9252 solver.cpp:406] Test net output #42: loss/loss21 = 7.5247e-05 (* 0.0454545 = 3.42032e-06 loss) | |
| I0404 14:47:40.460886 9252 solver.cpp:406] Test net output #43: loss/loss22 = 7.94584e-05 (* 0.0454545 = 3.61174e-06 loss) | |
| I0404 14:47:40.460897 9252 solver.cpp:406] Test net output #44: total_accuracy = 0.002 | |
| I0404 14:47:40.460908 9252 solver.cpp:406] Test net output #45: total_confidence = 0.000286593 | |
| I0404 14:47:40.496191 9252 solver.cpp:229] Iteration 50000, loss = 0.863015 | |
| I0404 14:47:40.496229 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 14:47:40.496248 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:47:40.496261 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 14:47:40.496273 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 14:47:40.496285 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 14:47:40.496297 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 14:47:40.496309 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 14:47:40.496320 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:47:40.496332 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:47:40.496343 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:47:40.496356 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:47:40.496366 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:47:40.496378 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:47:40.496389 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:47:40.496400 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:47:40.496412 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:47:40.496423 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:47:40.496434 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:47:40.496445 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:47:40.496457 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:47:40.496469 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:47:40.496480 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:47:40.496495 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.48135 (* 0.0454545 = 0.112788 loss) | |
| I0404 14:47:40.496510 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.90563 (* 0.0454545 = 0.132074 loss) | |
| I0404 14:47:40.496523 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.87837 (* 0.0454545 = 0.130835 loss) | |
| I0404 14:47:40.496536 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.20239 (* 0.0454545 = 0.145563 loss) | |
| I0404 14:47:40.496551 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.78568 (* 0.0454545 = 0.126622 loss) | |
| I0404 14:47:40.496563 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.37651 (* 0.0454545 = 0.108023 loss) | |
| I0404 14:47:40.496577 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.23471 (* 0.0454545 = 0.056123 loss) | |
| I0404 14:47:40.496590 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.597234 (* 0.0454545 = 0.027147 loss) | |
| I0404 14:47:40.496604 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.362306 (* 0.0454545 = 0.0164685 loss) | |
| I0404 14:47:40.496635 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.246652 (* 0.0454545 = 0.0112115 loss) | |
| I0404 14:47:40.496650 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.19769e-05 (* 0.0454545 = 2.81713e-06 loss) | |
| I0404 14:47:40.496665 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.53603e-05 (* 0.0454545 = 2.51638e-06 loss) | |
| I0404 14:47:40.496678 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.4087e-05 (* 0.0454545 = 2.4585e-06 loss) | |
| I0404 14:47:40.496692 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.92671e-05 (* 0.0454545 = 2.69396e-06 loss) | |
| I0404 14:47:40.496706 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.67745e-05 (* 0.0454545 = 2.58066e-06 loss) | |
| I0404 14:47:40.496721 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.72243e-05 (* 0.0454545 = 2.60111e-06 loss) | |
| I0404 14:47:40.496734 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.33217e-05 (* 0.0454545 = 2.87826e-06 loss) | |
| I0404 14:47:40.496748 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.0445e-05 (* 0.0454545 = 2.29295e-06 loss) | |
| I0404 14:47:40.496762 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.59607e-05 (* 0.0454545 = 2.54367e-06 loss) | |
| I0404 14:47:40.496775 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.75982e-05 (* 0.0454545 = 2.6181e-06 loss) | |
| I0404 14:47:40.496789 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.49716e-05 (* 0.0454545 = 2.49871e-06 loss) | |
| I0404 14:47:40.496803 9252 solver.cpp:245] Train net output #43: loss/loss22 = 5.92373e-05 (* 0.0454545 = 2.6926e-06 loss) | |
| I0404 14:47:40.496814 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:47:40.496825 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000627326 | |
| I0404 14:47:40.496840 9252 sgd_solver.cpp:106] Iteration 50000, lr = 0.0095 | |
| I0404 14:48:51.891000 9252 solver.cpp:229] Iteration 50500, loss = 0.862217 | |
| I0404 14:48:51.891181 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 14:48:51.891201 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 14:48:51.891214 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 14:48:51.891227 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 14:48:51.891243 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 14:48:51.891255 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.28125 | |
| I0404 14:48:51.891268 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 14:48:51.891279 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:48:51.891290 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 14:48:51.891302 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 14:48:51.891314 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:48:51.891326 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:48:51.891337 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:48:51.891350 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:48:51.891360 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:48:51.891372 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:48:51.891389 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:48:51.891401 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:48:51.891412 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:48:51.891423 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:48:51.891434 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:48:51.891451 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:48:51.891468 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.58265 (* 0.0454545 = 0.117393 loss) | |
| I0404 14:48:51.891482 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.10461 (* 0.0454545 = 0.141119 loss) | |
| I0404 14:48:51.891496 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.20927 (* 0.0454545 = 0.145876 loss) | |
| I0404 14:48:51.891510 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.4889 (* 0.0454545 = 0.158586 loss) | |
| I0404 14:48:51.891523 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.00319 (* 0.0454545 = 0.136509 loss) | |
| I0404 14:48:51.891536 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.70166 (* 0.0454545 = 0.122803 loss) | |
| I0404 14:48:51.891549 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.29955 (* 0.0454545 = 0.0590706 loss) | |
| I0404 14:48:51.891563 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.542259 (* 0.0454545 = 0.0246481 loss) | |
| I0404 14:48:51.891577 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.457863 (* 0.0454545 = 0.0208119 loss) | |
| I0404 14:48:51.891590 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.253872 (* 0.0454545 = 0.0115396 loss) | |
| I0404 14:48:51.891604 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.35378e-05 (* 0.0454545 = 2.88808e-06 loss) | |
| I0404 14:48:51.891618 9252 solver.cpp:245] Train net output #33: loss/loss12 = 6.28716e-05 (* 0.0454545 = 2.8578e-06 loss) | |
| I0404 14:48:51.891633 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.6682e-05 (* 0.0454545 = 3.031e-06 loss) | |
| I0404 14:48:51.891646 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.01766e-05 (* 0.0454545 = 3.18985e-06 loss) | |
| I0404 14:48:51.891660 9252 solver.cpp:245] Train net output #36: loss/loss15 = 6.31562e-05 (* 0.0454545 = 2.87074e-06 loss) | |
| I0404 14:48:51.891674 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.54041e-05 (* 0.0454545 = 2.97291e-06 loss) | |
| I0404 14:48:51.891688 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.16058e-05 (* 0.0454545 = 2.80026e-06 loss) | |
| I0404 14:48:51.891716 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.82985e-05 (* 0.0454545 = 2.64993e-06 loss) | |
| I0404 14:48:51.891731 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.81344e-05 (* 0.0454545 = 2.64247e-06 loss) | |
| I0404 14:48:51.891748 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.11755e-05 (* 0.0454545 = 2.7807e-06 loss) | |
| I0404 14:48:51.891762 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.09347e-05 (* 0.0454545 = 2.76976e-06 loss) | |
| I0404 14:48:51.891777 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.35565e-05 (* 0.0454545 = 2.88893e-06 loss) | |
| I0404 14:48:51.891788 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:48:51.891800 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000481547 | |
| I0404 14:48:51.891816 9252 sgd_solver.cpp:106] Iteration 50500, lr = 0.009495 | |
| I0404 14:50:03.061259 9252 solver.cpp:229] Iteration 51000, loss = 0.860943 | |
| I0404 14:50:03.061393 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.3125 | |
| I0404 14:50:03.061414 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 14:50:03.061425 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 14:50:03.061437 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 14:50:03.061450 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 14:50:03.061462 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 14:50:03.061473 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 14:50:03.061486 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:50:03.061497 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:50:03.061509 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:50:03.061534 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:50:03.061547 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:50:03.061559 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:50:03.061570 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:50:03.061583 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:50:03.061594 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:50:03.061605 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:50:03.061617 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:50:03.061628 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:50:03.061640 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:50:03.061651 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:50:03.061663 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:50:03.061679 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.59596 (* 0.0454545 = 0.117998 loss) | |
| I0404 14:50:03.061693 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.06103 (* 0.0454545 = 0.139138 loss) | |
| I0404 14:50:03.061707 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.17008 (* 0.0454545 = 0.144094 loss) | |
| I0404 14:50:03.061722 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.3543 (* 0.0454545 = 0.152468 loss) | |
| I0404 14:50:03.061734 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.8395 (* 0.0454545 = 0.129068 loss) | |
| I0404 14:50:03.061751 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.5652 (* 0.0454545 = 0.1166 loss) | |
| I0404 14:50:03.061765 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.10876 (* 0.0454545 = 0.0503982 loss) | |
| I0404 14:50:03.061779 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.353751 (* 0.0454545 = 0.0160796 loss) | |
| I0404 14:50:03.061794 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.163193 (* 0.0454545 = 0.00741786 loss) | |
| I0404 14:50:03.061807 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00711217 (* 0.0454545 = 0.000323281 loss) | |
| I0404 14:50:03.061821 9252 solver.cpp:245] Train net output #32: loss/loss11 = 9.07724e-05 (* 0.0454545 = 4.12602e-06 loss) | |
| I0404 14:50:03.061836 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.25417e-05 (* 0.0454545 = 3.29735e-06 loss) | |
| I0404 14:50:03.061849 9252 solver.cpp:245] Train net output #34: loss/loss13 = 8.07737e-05 (* 0.0454545 = 3.67153e-06 loss) | |
| I0404 14:50:03.061863 9252 solver.cpp:245] Train net output #35: loss/loss14 = 8.37988e-05 (* 0.0454545 = 3.80903e-06 loss) | |
| I0404 14:50:03.061883 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.99151e-05 (* 0.0454545 = 3.63251e-06 loss) | |
| I0404 14:50:03.061897 9252 solver.cpp:245] Train net output #37: loss/loss16 = 8.4117e-05 (* 0.0454545 = 3.8235e-06 loss) | |
| I0404 14:50:03.061913 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.83434e-05 (* 0.0454545 = 3.56106e-06 loss) | |
| I0404 14:50:03.061954 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.76517e-05 (* 0.0454545 = 3.52962e-06 loss) | |
| I0404 14:50:03.061969 9252 solver.cpp:245] Train net output #40: loss/loss19 = 8.1024e-05 (* 0.0454545 = 3.68291e-06 loss) | |
| I0404 14:50:03.061983 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.66141e-05 (* 0.0454545 = 3.48246e-06 loss) | |
| I0404 14:50:03.061997 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.40386e-05 (* 0.0454545 = 3.36539e-06 loss) | |
| I0404 14:50:03.062011 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.30808e-05 (* 0.0454545 = 3.32186e-06 loss) | |
| I0404 14:50:03.062022 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 14:50:03.062033 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000189852 | |
| I0404 14:50:03.062048 9252 sgd_solver.cpp:106] Iteration 51000, lr = 0.00949 | |
| I0404 14:51:13.263602 9252 solver.cpp:229] Iteration 51500, loss = 0.853104 | |
| I0404 14:51:13.263741 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 14:51:13.263761 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 14:51:13.263774 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 14:51:13.263787 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 14:51:13.263799 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 14:51:13.263811 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 14:51:13.263823 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.59375 | |
| I0404 14:51:13.263835 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 14:51:13.263847 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:51:13.263859 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:51:13.263870 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:51:13.263882 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:51:13.263895 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:51:13.263906 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:51:13.263917 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:51:13.263928 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:51:13.263941 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:51:13.263952 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:51:13.263963 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:51:13.263974 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:51:13.263985 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:51:13.263996 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:51:13.264013 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.33535 (* 0.0454545 = 0.106152 loss) | |
| I0404 14:51:13.264026 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.07689 (* 0.0454545 = 0.139859 loss) | |
| I0404 14:51:13.264040 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.08509 (* 0.0454545 = 0.140231 loss) | |
| I0404 14:51:13.264055 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.14684 (* 0.0454545 = 0.143038 loss) | |
| I0404 14:51:13.264068 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.80472 (* 0.0454545 = 0.127487 loss) | |
| I0404 14:51:13.264081 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.44849 (* 0.0454545 = 0.111295 loss) | |
| I0404 14:51:13.264096 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.32111 (* 0.0454545 = 0.0600503 loss) | |
| I0404 14:51:13.264109 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.427917 (* 0.0454545 = 0.0194508 loss) | |
| I0404 14:51:13.264123 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.136373 (* 0.0454545 = 0.00619878 loss) | |
| I0404 14:51:13.264137 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00670748 (* 0.0454545 = 0.000304886 loss) | |
| I0404 14:51:13.264153 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.90821e-05 (* 0.0454545 = 1.32191e-06 loss) | |
| I0404 14:51:13.264166 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.86243e-05 (* 0.0454545 = 1.3011e-06 loss) | |
| I0404 14:51:13.264180 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.45993e-05 (* 0.0454545 = 1.11815e-06 loss) | |
| I0404 14:51:13.264194 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.44841e-05 (* 0.0454545 = 1.11291e-06 loss) | |
| I0404 14:51:13.264209 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.7292e-05 (* 0.0454545 = 1.24055e-06 loss) | |
| I0404 14:51:13.264222 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.10834e-05 (* 0.0454545 = 1.41288e-06 loss) | |
| I0404 14:51:13.264235 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.74428e-05 (* 0.0454545 = 1.2474e-06 loss) | |
| I0404 14:51:13.264268 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.38912e-05 (* 0.0454545 = 1.08596e-06 loss) | |
| I0404 14:51:13.264286 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.87701e-05 (* 0.0454545 = 1.30773e-06 loss) | |
| I0404 14:51:13.264299 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.68206e-05 (* 0.0454545 = 1.21912e-06 loss) | |
| I0404 14:51:13.264313 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.63047e-05 (* 0.0454545 = 1.19567e-06 loss) | |
| I0404 14:51:13.264328 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.90858e-05 (* 0.0454545 = 1.32208e-06 loss) | |
| I0404 14:51:13.264339 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:51:13.264350 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000231739 | |
| I0404 14:51:13.264374 9252 sgd_solver.cpp:106] Iteration 51500, lr = 0.009485 | |
| I0404 14:52:25.145463 9252 solver.cpp:229] Iteration 52000, loss = 0.854467 | |
| I0404 14:52:25.145588 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.34375 | |
| I0404 14:52:25.145607 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 14:52:25.145620 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 14:52:25.145632 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 14:52:25.145644 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 14:52:25.145658 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 14:52:25.145669 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 14:52:25.145680 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 14:52:25.145692 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:52:25.145704 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:52:25.145715 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:52:25.145727 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:52:25.145740 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:52:25.145753 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:52:25.145766 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:52:25.145777 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:52:25.145789 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:52:25.145800 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:52:25.145812 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:52:25.145823 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:52:25.145834 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:52:25.145845 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:52:25.145861 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.38404 (* 0.0454545 = 0.108366 loss) | |
| I0404 14:52:25.145875 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.16047 (* 0.0454545 = 0.143658 loss) | |
| I0404 14:52:25.145889 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.92068 (* 0.0454545 = 0.132758 loss) | |
| I0404 14:52:25.145902 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.97073 (* 0.0454545 = 0.135033 loss) | |
| I0404 14:52:25.145915 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.64579 (* 0.0454545 = 0.120263 loss) | |
| I0404 14:52:25.145930 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.14017 (* 0.0454545 = 0.0972803 loss) | |
| I0404 14:52:25.145942 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.42803 (* 0.0454545 = 0.0649105 loss) | |
| I0404 14:52:25.145956 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.68858 (* 0.0454545 = 0.0312991 loss) | |
| I0404 14:52:25.145970 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.11219 (* 0.0454545 = 0.00509955 loss) | |
| I0404 14:52:25.145983 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0959211 (* 0.0454545 = 0.00436005 loss) | |
| I0404 14:52:25.145998 9252 solver.cpp:245] Train net output #32: loss/loss11 = 5.25331e-05 (* 0.0454545 = 2.38787e-06 loss) | |
| I0404 14:52:25.146013 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.76228e-05 (* 0.0454545 = 2.16467e-06 loss) | |
| I0404 14:52:25.146026 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.67563e-05 (* 0.0454545 = 2.12529e-06 loss) | |
| I0404 14:52:25.146039 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.9316e-05 (* 0.0454545 = 2.24164e-06 loss) | |
| I0404 14:52:25.146054 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.52158e-05 (* 0.0454545 = 2.05526e-06 loss) | |
| I0404 14:52:25.146067 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.43068e-05 (* 0.0454545 = 2.46849e-06 loss) | |
| I0404 14:52:25.146081 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.61025e-05 (* 0.0454545 = 2.09557e-06 loss) | |
| I0404 14:52:25.146112 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.70301e-05 (* 0.0454545 = 2.13773e-06 loss) | |
| I0404 14:52:25.146127 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.62647e-05 (* 0.0454545 = 2.10294e-06 loss) | |
| I0404 14:52:25.146142 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.39268e-05 (* 0.0454545 = 1.99667e-06 loss) | |
| I0404 14:52:25.146154 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.40645e-05 (* 0.0454545 = 2.00293e-06 loss) | |
| I0404 14:52:25.146168 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.70154e-05 (* 0.0454545 = 2.13707e-06 loss) | |
| I0404 14:52:25.146180 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:52:25.146191 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00318399 | |
| I0404 14:52:25.146205 9252 sgd_solver.cpp:106] Iteration 52000, lr = 0.00948 | |
| I0404 14:53:36.193087 9252 solver.cpp:229] Iteration 52500, loss = 0.852728 | |
| I0404 14:53:36.193253 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 14:53:36.193274 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:53:36.193286 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 14:53:36.193306 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 14:53:36.193318 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 14:53:36.193331 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 14:53:36.193342 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 14:53:36.193354 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 14:53:36.193367 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 14:53:36.193378 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:53:36.193389 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:53:36.193402 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:53:36.193413 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:53:36.193454 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:53:36.193477 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:53:36.193491 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:53:36.193502 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:53:36.193514 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:53:36.193526 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:53:36.193537 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:53:36.193548 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:53:36.193560 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:53:36.193575 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.83369 (* 0.0454545 = 0.128804 loss) | |
| I0404 14:53:36.193590 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.05721 (* 0.0454545 = 0.138964 loss) | |
| I0404 14:53:36.193604 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.33036 (* 0.0454545 = 0.15138 loss) | |
| I0404 14:53:36.193619 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.52619 (* 0.0454545 = 0.160282 loss) | |
| I0404 14:53:36.193632 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.85973 (* 0.0454545 = 0.129988 loss) | |
| I0404 14:53:36.193646 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.04031 (* 0.0454545 = 0.0927412 loss) | |
| I0404 14:53:36.193660 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.09889 (* 0.0454545 = 0.0499495 loss) | |
| I0404 14:53:36.193675 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.185643 (* 0.0454545 = 0.00843832 loss) | |
| I0404 14:53:36.193689 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0035933 (* 0.0454545 = 0.000163332 loss) | |
| I0404 14:53:36.193703 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00138296 (* 0.0454545 = 6.28619e-05 loss) | |
| I0404 14:53:36.193718 9252 solver.cpp:245] Train net output #32: loss/loss11 = 7.83438e-06 (* 0.0454545 = 3.56108e-07 loss) | |
| I0404 14:53:36.193732 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.3501e-06 (* 0.0454545 = 3.34095e-07 loss) | |
| I0404 14:53:36.193748 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.21227e-06 (* 0.0454545 = 3.27831e-07 loss) | |
| I0404 14:53:36.193763 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.68697e-06 (* 0.0454545 = 3.03953e-07 loss) | |
| I0404 14:53:36.193778 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.95365e-06 (* 0.0454545 = 3.61529e-07 loss) | |
| I0404 14:53:36.193791 9252 solver.cpp:245] Train net output #37: loss/loss16 = 8.14734e-06 (* 0.0454545 = 3.70333e-07 loss) | |
| I0404 14:53:36.193805 9252 solver.cpp:245] Train net output #38: loss/loss17 = 8.08028e-06 (* 0.0454545 = 3.67285e-07 loss) | |
| I0404 14:53:36.193835 9252 solver.cpp:245] Train net output #39: loss/loss18 = 8.03188e-06 (* 0.0454545 = 3.65086e-07 loss) | |
| I0404 14:53:36.193850 9252 solver.cpp:245] Train net output #40: loss/loss19 = 8.03186e-06 (* 0.0454545 = 3.65085e-07 loss) | |
| I0404 14:53:36.193863 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.55874e-06 (* 0.0454545 = 3.43579e-07 loss) | |
| I0404 14:53:36.193877 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.85304e-06 (* 0.0454545 = 3.56956e-07 loss) | |
| I0404 14:53:36.193891 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.56991e-06 (* 0.0454545 = 3.44087e-07 loss) | |
| I0404 14:53:36.193902 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:53:36.193914 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000120082 | |
| I0404 14:53:36.193928 9252 sgd_solver.cpp:106] Iteration 52500, lr = 0.009475 | |
| I0404 14:54:47.737259 9252 solver.cpp:229] Iteration 53000, loss = 0.851166 | |
| I0404 14:54:47.737378 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.3125 | |
| I0404 14:54:47.737399 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 14:54:47.737411 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 14:54:47.737424 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 14:54:47.737437 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 14:54:47.737454 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.3125 | |
| I0404 14:54:47.737467 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 14:54:47.737478 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:54:47.737490 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.875 | |
| I0404 14:54:47.737514 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:54:47.737529 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:54:47.737547 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:54:47.737560 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:54:47.737571 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:54:47.737582 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:54:47.737594 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:54:47.737606 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:54:47.737617 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:54:47.737629 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:54:47.737640 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:54:47.737653 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:54:47.737663 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:54:47.737679 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.96242 (* 0.0454545 = 0.134655 loss) | |
| I0404 14:54:47.737694 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.8705 (* 0.0454545 = 0.130477 loss) | |
| I0404 14:54:47.737707 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.04621 (* 0.0454545 = 0.138464 loss) | |
| I0404 14:54:47.737721 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.84617 (* 0.0454545 = 0.129372 loss) | |
| I0404 14:54:47.737735 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.64364 (* 0.0454545 = 0.120166 loss) | |
| I0404 14:54:47.737751 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.06513 (* 0.0454545 = 0.139324 loss) | |
| I0404 14:54:47.737766 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.976445 (* 0.0454545 = 0.0443839 loss) | |
| I0404 14:54:47.737778 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.466647 (* 0.0454545 = 0.0212112 loss) | |
| I0404 14:54:47.737792 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.436749 (* 0.0454545 = 0.0198522 loss) | |
| I0404 14:54:47.737807 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.240015 (* 0.0454545 = 0.0109098 loss) | |
| I0404 14:54:47.737820 9252 solver.cpp:245] Train net output #32: loss/loss11 = 0.000150124 (* 0.0454545 = 6.82384e-06 loss) | |
| I0404 14:54:47.737834 9252 solver.cpp:245] Train net output #33: loss/loss12 = 0.000128319 (* 0.0454545 = 5.83269e-06 loss) | |
| I0404 14:54:47.737848 9252 solver.cpp:245] Train net output #34: loss/loss13 = 0.000138342 (* 0.0454545 = 6.28825e-06 loss) | |
| I0404 14:54:47.737862 9252 solver.cpp:245] Train net output #35: loss/loss14 = 0.000134982 (* 0.0454545 = 6.13556e-06 loss) | |
| I0404 14:54:47.737876 9252 solver.cpp:245] Train net output #36: loss/loss15 = 0.000107872 (* 0.0454545 = 4.90326e-06 loss) | |
| I0404 14:54:47.737890 9252 solver.cpp:245] Train net output #37: loss/loss16 = 0.000124299 (* 0.0454545 = 5.64994e-06 loss) | |
| I0404 14:54:47.737905 9252 solver.cpp:245] Train net output #38: loss/loss17 = 0.000122286 (* 0.0454545 = 5.55847e-06 loss) | |
| I0404 14:54:47.737936 9252 solver.cpp:245] Train net output #39: loss/loss18 = 0.000138766 (* 0.0454545 = 6.30754e-06 loss) | |
| I0404 14:54:47.737951 9252 solver.cpp:245] Train net output #40: loss/loss19 = 0.000108042 (* 0.0454545 = 4.91099e-06 loss) | |
| I0404 14:54:47.737965 9252 solver.cpp:245] Train net output #41: loss/loss20 = 0.000126679 (* 0.0454545 = 5.75814e-06 loss) | |
| I0404 14:54:47.737979 9252 solver.cpp:245] Train net output #42: loss/loss21 = 0.00011202 (* 0.0454545 = 5.09181e-06 loss) | |
| I0404 14:54:47.737993 9252 solver.cpp:245] Train net output #43: loss/loss22 = 0.000118676 (* 0.0454545 = 5.39436e-06 loss) | |
| I0404 14:54:47.738005 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:54:47.738016 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000308495 | |
| I0404 14:54:47.738030 9252 sgd_solver.cpp:106] Iteration 53000, lr = 0.00947 | |
| I0404 14:55:58.120441 9252 solver.cpp:229] Iteration 53500, loss = 0.84983 | |
| I0404 14:55:58.120677 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.3125 | |
| I0404 14:55:58.120698 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 14:55:58.120712 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.1875 | |
| I0404 14:55:58.120723 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 14:55:58.120736 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 14:55:58.120748 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 14:55:58.120760 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 14:55:58.120772 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 1 | |
| I0404 14:55:58.120785 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 14:55:58.120797 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:55:58.120808 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:55:58.120820 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:55:58.120831 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:55:58.120842 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:55:58.120854 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:55:58.120865 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:55:58.120877 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:55:58.120888 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:55:58.120903 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:55:58.120914 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:55:58.120926 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:55:58.120939 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:55:58.120954 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.45613 (* 0.0454545 = 0.111642 loss) | |
| I0404 14:55:58.120968 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.845 (* 0.0454545 = 0.129318 loss) | |
| I0404 14:55:58.120982 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.095 (* 0.0454545 = 0.140682 loss) | |
| I0404 14:55:58.120996 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.94146 (* 0.0454545 = 0.133703 loss) | |
| I0404 14:55:58.121011 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.74849 (* 0.0454545 = 0.124931 loss) | |
| I0404 14:55:58.121024 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.42133 (* 0.0454545 = 0.11006 loss) | |
| I0404 14:55:58.121037 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.955905 (* 0.0454545 = 0.0434502 loss) | |
| I0404 14:55:58.121052 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.123595 (* 0.0454545 = 0.00561796 loss) | |
| I0404 14:55:58.121067 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0158087 (* 0.0454545 = 0.000718575 loss) | |
| I0404 14:55:58.121080 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00526253 (* 0.0454545 = 0.000239206 loss) | |
| I0404 14:55:58.121094 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.14307e-05 (* 0.0454545 = 2.79231e-06 loss) | |
| I0404 14:55:58.121109 9252 solver.cpp:245] Train net output #33: loss/loss12 = 6.66652e-05 (* 0.0454545 = 3.03024e-06 loss) | |
| I0404 14:55:58.121122 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.59486e-05 (* 0.0454545 = 2.99766e-06 loss) | |
| I0404 14:55:58.121136 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.45635e-05 (* 0.0454545 = 2.9347e-06 loss) | |
| I0404 14:55:58.121150 9252 solver.cpp:245] Train net output #36: loss/loss15 = 6.03904e-05 (* 0.0454545 = 2.74502e-06 loss) | |
| I0404 14:55:58.121163 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.10507e-05 (* 0.0454545 = 2.77503e-06 loss) | |
| I0404 14:55:58.121177 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.66865e-05 (* 0.0454545 = 2.57666e-06 loss) | |
| I0404 14:55:58.121209 9252 solver.cpp:245] Train net output #39: loss/loss18 = 6.28145e-05 (* 0.0454545 = 2.85521e-06 loss) | |
| I0404 14:55:58.121224 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.12166e-05 (* 0.0454545 = 2.78257e-06 loss) | |
| I0404 14:55:58.121238 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.73991e-05 (* 0.0454545 = 2.60905e-06 loss) | |
| I0404 14:55:58.121253 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.37491e-05 (* 0.0454545 = 2.44314e-06 loss) | |
| I0404 14:55:58.121266 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.07019e-05 (* 0.0454545 = 2.75918e-06 loss) | |
| I0404 14:55:58.121279 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:55:58.121290 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000312977 | |
| I0404 14:55:58.121304 9252 sgd_solver.cpp:106] Iteration 53500, lr = 0.009465 | |
| I0404 14:57:09.687680 9252 solver.cpp:229] Iteration 54000, loss = 0.847134 | |
| I0404 14:57:09.687816 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 14:57:09.687836 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 14:57:09.687849 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 14:57:09.687861 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 14:57:09.687873 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.375 | |
| I0404 14:57:09.687885 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 14:57:09.687897 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 14:57:09.687911 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 14:57:09.687922 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 14:57:09.687934 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:57:09.687947 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:57:09.687958 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:57:09.687976 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:57:09.687988 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:57:09.687999 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:57:09.688010 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:57:09.688022 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:57:09.688035 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:57:09.688055 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:57:09.688066 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:57:09.688077 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:57:09.688088 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:57:09.688104 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.63263 (* 0.0454545 = 0.119665 loss) | |
| I0404 14:57:09.688119 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.06191 (* 0.0454545 = 0.139178 loss) | |
| I0404 14:57:09.688133 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.95605 (* 0.0454545 = 0.134366 loss) | |
| I0404 14:57:09.688148 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.99376 (* 0.0454545 = 0.13608 loss) | |
| I0404 14:57:09.688161 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.43351 (* 0.0454545 = 0.110614 loss) | |
| I0404 14:57:09.688174 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.47112 (* 0.0454545 = 0.112324 loss) | |
| I0404 14:57:09.688189 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.6929 (* 0.0454545 = 0.0769498 loss) | |
| I0404 14:57:09.688202 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.755683 (* 0.0454545 = 0.0343492 loss) | |
| I0404 14:57:09.688216 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0517068 (* 0.0454545 = 0.00235031 loss) | |
| I0404 14:57:09.688230 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0148996 (* 0.0454545 = 0.000677255 loss) | |
| I0404 14:57:09.688246 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.34927e-05 (* 0.0454545 = 2.88603e-06 loss) | |
| I0404 14:57:09.688259 9252 solver.cpp:245] Train net output #33: loss/loss12 = 6.31097e-05 (* 0.0454545 = 2.86862e-06 loss) | |
| I0404 14:57:09.688273 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.30247e-05 (* 0.0454545 = 2.41021e-06 loss) | |
| I0404 14:57:09.688287 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.45916e-05 (* 0.0454545 = 2.93598e-06 loss) | |
| I0404 14:57:09.688302 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.07454e-05 (* 0.0454545 = 2.30661e-06 loss) | |
| I0404 14:57:09.688315 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.33759e-05 (* 0.0454545 = 2.42618e-06 loss) | |
| I0404 14:57:09.688329 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.17397e-05 (* 0.0454545 = 2.3518e-06 loss) | |
| I0404 14:57:09.688361 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.94168e-05 (* 0.0454545 = 2.24622e-06 loss) | |
| I0404 14:57:09.688376 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.32583e-05 (* 0.0454545 = 2.42083e-06 loss) | |
| I0404 14:57:09.688390 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.1644e-05 (* 0.0454545 = 2.34745e-06 loss) | |
| I0404 14:57:09.688405 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.2665e-05 (* 0.0454545 = 2.39386e-06 loss) | |
| I0404 14:57:09.688418 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.41354e-05 (* 0.0454545 = 2.00616e-06 loss) | |
| I0404 14:57:09.688431 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:57:09.688441 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00033329 | |
| I0404 14:57:09.688454 9252 sgd_solver.cpp:106] Iteration 54000, lr = 0.00946 | |
| I0404 14:58:20.873844 9252 solver.cpp:229] Iteration 54500, loss = 0.843077 | |
| I0404 14:58:20.873994 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.4375 | |
| I0404 14:58:20.874014 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 14:58:20.874027 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.25 | |
| I0404 14:58:20.874040 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 14:58:20.874052 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.5625 | |
| I0404 14:58:20.874070 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 14:58:20.874081 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 14:58:20.874094 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 14:58:20.874105 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 14:58:20.874117 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 14:58:20.874130 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:58:20.874140 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:58:20.874152 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:58:20.874163 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:58:20.874176 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:58:20.874186 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:58:20.874197 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:58:20.874209 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:58:20.874220 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:58:20.874231 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:58:20.874243 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:58:20.874254 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:58:20.874269 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.9395 (* 0.0454545 = 0.0881592 loss) | |
| I0404 14:58:20.874284 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.65188 (* 0.0454545 = 0.12054 loss) | |
| I0404 14:58:20.874299 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.581 (* 0.0454545 = 0.117318 loss) | |
| I0404 14:58:20.874312 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.76194 (* 0.0454545 = 0.125543 loss) | |
| I0404 14:58:20.874326 9252 solver.cpp:245] Train net output #26: loss/loss05 = 1.81682 (* 0.0454545 = 0.0825829 loss) | |
| I0404 14:58:20.874339 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.66661 (* 0.0454545 = 0.0757549 loss) | |
| I0404 14:58:20.874353 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.14931 (* 0.0454545 = 0.0522414 loss) | |
| I0404 14:58:20.874366 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.42436 (* 0.0454545 = 0.0192891 loss) | |
| I0404 14:58:20.874380 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.412434 (* 0.0454545 = 0.018747 loss) | |
| I0404 14:58:20.874394 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0520824 (* 0.0454545 = 0.00236738 loss) | |
| I0404 14:58:20.874408 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.434e-05 (* 0.0454545 = 2.01545e-06 loss) | |
| I0404 14:58:20.874423 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.72386e-05 (* 0.0454545 = 1.69266e-06 loss) | |
| I0404 14:58:20.874436 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.04856e-05 (* 0.0454545 = 1.84026e-06 loss) | |
| I0404 14:58:20.874450 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.99287e-05 (* 0.0454545 = 1.81494e-06 loss) | |
| I0404 14:58:20.874464 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.68642e-05 (* 0.0454545 = 1.67565e-06 loss) | |
| I0404 14:58:20.874477 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.36489e-05 (* 0.0454545 = 1.98404e-06 loss) | |
| I0404 14:58:20.874491 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.70354e-05 (* 0.0454545 = 1.68343e-06 loss) | |
| I0404 14:58:20.874519 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.80209e-05 (* 0.0454545 = 1.72822e-06 loss) | |
| I0404 14:58:20.874534 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.95206e-05 (* 0.0454545 = 1.79639e-06 loss) | |
| I0404 14:58:20.874548 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.58954e-05 (* 0.0454545 = 1.63161e-06 loss) | |
| I0404 14:58:20.874562 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.56029e-05 (* 0.0454545 = 1.61831e-06 loss) | |
| I0404 14:58:20.874577 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.57482e-05 (* 0.0454545 = 1.62492e-06 loss) | |
| I0404 14:58:20.874588 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:58:20.874599 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000556105 | |
| I0404 14:58:20.874613 9252 sgd_solver.cpp:106] Iteration 54500, lr = 0.009455 | |
| I0404 14:59:32.774508 9252 solver.cpp:229] Iteration 55000, loss = 0.848519 | |
| I0404 14:59:32.774631 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.3125 | |
| I0404 14:59:32.774651 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.25 | |
| I0404 14:59:32.774663 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 14:59:32.774675 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 14:59:32.774688 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 14:59:32.774699 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 14:59:32.774711 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 14:59:32.774723 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 14:59:32.774734 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 14:59:32.774746 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 14:59:32.774757 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 14:59:32.774770 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 14:59:32.774781 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 14:59:32.774792 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 14:59:32.774804 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 14:59:32.774816 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 14:59:32.774827 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 14:59:32.774839 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 14:59:32.774850 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 14:59:32.774863 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 14:59:32.774873 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 14:59:32.774885 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 14:59:32.774904 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.51084 (* 0.0454545 = 0.114129 loss) | |
| I0404 14:59:32.774919 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.73646 (* 0.0454545 = 0.124384 loss) | |
| I0404 14:59:32.774932 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.94627 (* 0.0454545 = 0.133921 loss) | |
| I0404 14:59:32.774946 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.8845 (* 0.0454545 = 0.131114 loss) | |
| I0404 14:59:32.774960 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.72206 (* 0.0454545 = 0.12373 loss) | |
| I0404 14:59:32.774973 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.37868 (* 0.0454545 = 0.108122 loss) | |
| I0404 14:59:32.774987 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.2195 (* 0.0454545 = 0.055432 loss) | |
| I0404 14:59:32.775001 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.416476 (* 0.0454545 = 0.0189307 loss) | |
| I0404 14:59:32.775015 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.15298 (* 0.0454545 = 0.00695361 loss) | |
| I0404 14:59:32.775032 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.143107 (* 0.0454545 = 0.00650486 loss) | |
| I0404 14:59:32.775046 9252 solver.cpp:245] Train net output #32: loss/loss11 = 7.4198e-05 (* 0.0454545 = 3.37263e-06 loss) | |
| I0404 14:59:32.775061 9252 solver.cpp:245] Train net output #33: loss/loss12 = 6.97379e-05 (* 0.0454545 = 3.16991e-06 loss) | |
| I0404 14:59:32.775075 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.02483e-05 (* 0.0454545 = 3.1931e-06 loss) | |
| I0404 14:59:32.775089 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.2302e-05 (* 0.0454545 = 3.28645e-06 loss) | |
| I0404 14:59:32.775104 9252 solver.cpp:245] Train net output #36: loss/loss15 = 6.72975e-05 (* 0.0454545 = 3.05898e-06 loss) | |
| I0404 14:59:32.775117 9252 solver.cpp:245] Train net output #37: loss/loss16 = 7.0645e-05 (* 0.0454545 = 3.21114e-06 loss) | |
| I0404 14:59:32.775131 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.36349e-05 (* 0.0454545 = 2.89249e-06 loss) | |
| I0404 14:59:32.775162 9252 solver.cpp:245] Train net output #39: loss/loss18 = 6.69079e-05 (* 0.0454545 = 3.04127e-06 loss) | |
| I0404 14:59:32.775178 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.4745e-05 (* 0.0454545 = 2.94296e-06 loss) | |
| I0404 14:59:32.775192 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.49126e-05 (* 0.0454545 = 2.95057e-06 loss) | |
| I0404 14:59:32.775207 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.43615e-05 (* 0.0454545 = 2.92552e-06 loss) | |
| I0404 14:59:32.775219 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.75858e-05 (* 0.0454545 = 3.07208e-06 loss) | |
| I0404 14:59:32.775233 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 14:59:32.775243 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000419656 | |
| I0404 14:59:32.775259 9252 sgd_solver.cpp:106] Iteration 55000, lr = 0.00945 | |
| I0404 15:00:43.382709 9252 solver.cpp:229] Iteration 55500, loss = 0.840133 | |
| I0404 15:00:43.382853 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.21875 | |
| I0404 15:00:43.382874 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.21875 | |
| I0404 15:00:43.382887 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 15:00:43.382900 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 15:00:43.382912 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.09375 | |
| I0404 15:00:43.382925 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.21875 | |
| I0404 15:00:43.382936 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.65625 | |
| I0404 15:00:43.382948 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:00:43.382961 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:00:43.382972 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:00:43.382984 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:00:43.382995 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:00:43.383008 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:00:43.383018 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:00:43.383030 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:00:43.383041 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:00:43.383054 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:00:43.383064 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:00:43.383076 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:00:43.383088 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:00:43.383100 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:00:43.383111 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:00:43.383126 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.8509 (* 0.0454545 = 0.129587 loss) | |
| I0404 15:00:43.383141 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.14138 (* 0.0454545 = 0.14279 loss) | |
| I0404 15:00:43.383155 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.28068 (* 0.0454545 = 0.149122 loss) | |
| I0404 15:00:43.383168 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.38746 (* 0.0454545 = 0.153975 loss) | |
| I0404 15:00:43.383183 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.93123 (* 0.0454545 = 0.133238 loss) | |
| I0404 15:00:43.383196 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.81643 (* 0.0454545 = 0.12802 loss) | |
| I0404 15:00:43.383210 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.27728 (* 0.0454545 = 0.058058 loss) | |
| I0404 15:00:43.383224 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.620098 (* 0.0454545 = 0.0281863 loss) | |
| I0404 15:00:43.383239 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.176225 (* 0.0454545 = 0.00801023 loss) | |
| I0404 15:00:43.383252 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0238801 (* 0.0454545 = 0.00108546 loss) | |
| I0404 15:00:43.383267 9252 solver.cpp:245] Train net output #32: loss/loss11 = 5.04252e-05 (* 0.0454545 = 2.29206e-06 loss) | |
| I0404 15:00:43.383281 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.88147e-05 (* 0.0454545 = 2.21885e-06 loss) | |
| I0404 15:00:43.383296 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.92888e-05 (* 0.0454545 = 2.2404e-06 loss) | |
| I0404 15:00:43.383309 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.47686e-05 (* 0.0454545 = 2.48948e-06 loss) | |
| I0404 15:00:43.383323 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.80678e-05 (* 0.0454545 = 2.1849e-06 loss) | |
| I0404 15:00:43.383337 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.16964e-05 (* 0.0454545 = 2.34984e-06 loss) | |
| I0404 15:00:43.383352 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.58176e-05 (* 0.0454545 = 2.08262e-06 loss) | |
| I0404 15:00:43.383383 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.06135e-05 (* 0.0454545 = 2.30061e-06 loss) | |
| I0404 15:00:43.383397 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.92106e-05 (* 0.0454545 = 2.23684e-06 loss) | |
| I0404 15:00:43.383411 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.9014e-05 (* 0.0454545 = 2.22791e-06 loss) | |
| I0404 15:00:43.383425 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.03319e-05 (* 0.0454545 = 2.28781e-06 loss) | |
| I0404 15:00:43.383440 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.77585e-05 (* 0.0454545 = 2.17084e-06 loss) | |
| I0404 15:00:43.383451 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:00:43.383462 9252 solver.cpp:245] Train net output #45: total_confidence = 2.89059e-05 | |
| I0404 15:00:43.383476 9252 sgd_solver.cpp:106] Iteration 55500, lr = 0.009445 | |
| I0404 15:01:55.444340 9252 solver.cpp:229] Iteration 56000, loss = 0.8449 | |
| I0404 15:01:55.444447 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.3125 | |
| I0404 15:01:55.444465 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 15:01:55.444478 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.1875 | |
| I0404 15:01:55.444491 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 15:01:55.444502 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 15:01:55.444514 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 15:01:55.444526 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 15:01:55.444538 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:01:55.444550 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:01:55.444562 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:01:55.444574 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:01:55.444586 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:01:55.444597 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:01:55.444609 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:01:55.444620 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:01:55.444631 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:01:55.444643 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:01:55.444654 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:01:55.444665 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:01:55.444684 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:01:55.444695 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:01:55.444707 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:01:55.444722 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.58832 (* 0.0454545 = 0.117651 loss) | |
| I0404 15:01:55.444736 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.69997 (* 0.0454545 = 0.122726 loss) | |
| I0404 15:01:55.444759 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.92015 (* 0.0454545 = 0.132734 loss) | |
| I0404 15:01:55.444773 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.932 (* 0.0454545 = 0.133273 loss) | |
| I0404 15:01:55.444787 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.30519 (* 0.0454545 = 0.104782 loss) | |
| I0404 15:01:55.444802 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.10255 (* 0.0454545 = 0.0955703 loss) | |
| I0404 15:01:55.444815 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.919075 (* 0.0454545 = 0.0417761 loss) | |
| I0404 15:01:55.444829 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.607411 (* 0.0454545 = 0.0276096 loss) | |
| I0404 15:01:55.444842 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.339584 (* 0.0454545 = 0.0154356 loss) | |
| I0404 15:01:55.444856 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.195459 (* 0.0454545 = 0.0088845 loss) | |
| I0404 15:01:55.444870 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.09494e-05 (* 0.0454545 = 1.86134e-06 loss) | |
| I0404 15:01:55.444885 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.06723e-05 (* 0.0454545 = 1.84874e-06 loss) | |
| I0404 15:01:55.444901 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.74245e-05 (* 0.0454545 = 1.70111e-06 loss) | |
| I0404 15:01:55.444916 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.90828e-05 (* 0.0454545 = 1.77649e-06 loss) | |
| I0404 15:01:55.444931 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.19204e-05 (* 0.0454545 = 1.90547e-06 loss) | |
| I0404 15:01:55.444944 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.44493e-05 (* 0.0454545 = 2.02042e-06 loss) | |
| I0404 15:01:55.444959 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.82224e-05 (* 0.0454545 = 1.73738e-06 loss) | |
| I0404 15:01:55.444990 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.79798e-05 (* 0.0454545 = 1.72635e-06 loss) | |
| I0404 15:01:55.445005 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.86099e-05 (* 0.0454545 = 1.75499e-06 loss) | |
| I0404 15:01:55.445019 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.01335e-05 (* 0.0454545 = 1.82425e-06 loss) | |
| I0404 15:01:55.445037 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.48961e-05 (* 0.0454545 = 1.58619e-06 loss) | |
| I0404 15:01:55.445050 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.71601e-05 (* 0.0454545 = 1.6891e-06 loss) | |
| I0404 15:01:55.445063 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:01:55.445075 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000419278 | |
| I0404 15:01:55.445091 9252 sgd_solver.cpp:106] Iteration 56000, lr = 0.00944 | |
| I0404 15:03:06.692260 9252 solver.cpp:229] Iteration 56500, loss = 0.835175 | |
| I0404 15:03:06.692406 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.3125 | |
| I0404 15:03:06.692432 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 15:03:06.692451 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 15:03:06.692464 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.28125 | |
| I0404 15:03:06.692476 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.5 | |
| I0404 15:03:06.692488 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.625 | |
| I0404 15:03:06.692500 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 15:03:06.692513 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.78125 | |
| I0404 15:03:06.692525 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:03:06.692536 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:03:06.692548 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:03:06.692559 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:03:06.692571 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:03:06.692582 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:03:06.692594 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:03:06.692605 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:03:06.692616 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:03:06.692627 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:03:06.692638 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:03:06.692649 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:03:06.692662 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:03:06.692672 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:03:06.692687 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.93431 (* 0.0454545 = 0.133378 loss) | |
| I0404 15:03:06.692701 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.89527 (* 0.0454545 = 0.131603 loss) | |
| I0404 15:03:06.692715 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.08027 (* 0.0454545 = 0.140012 loss) | |
| I0404 15:03:06.692729 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.89373 (* 0.0454545 = 0.131533 loss) | |
| I0404 15:03:06.692745 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.21685 (* 0.0454545 = 0.100766 loss) | |
| I0404 15:03:06.692760 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.64145 (* 0.0454545 = 0.0746111 loss) | |
| I0404 15:03:06.692775 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.20346 (* 0.0454545 = 0.0547026 loss) | |
| I0404 15:03:06.692788 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.845982 (* 0.0454545 = 0.0384537 loss) | |
| I0404 15:03:06.692809 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.236526 (* 0.0454545 = 0.0107512 loss) | |
| I0404 15:03:06.692839 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.19804 (* 0.0454545 = 0.00900183 loss) | |
| I0404 15:03:06.692870 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.49498e-05 (* 0.0454545 = 6.79538e-07 loss) | |
| I0404 15:03:06.692901 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.63916e-05 (* 0.0454545 = 7.45072e-07 loss) | |
| I0404 15:03:06.692924 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.34112e-05 (* 0.0454545 = 6.09602e-07 loss) | |
| I0404 15:03:06.692937 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.44916e-05 (* 0.0454545 = 6.5871e-07 loss) | |
| I0404 15:03:06.692951 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.44507e-05 (* 0.0454545 = 6.56848e-07 loss) | |
| I0404 15:03:06.692965 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.4473e-05 (* 0.0454545 = 6.57865e-07 loss) | |
| I0404 15:03:06.692980 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.41936e-05 (* 0.0454545 = 6.45164e-07 loss) | |
| I0404 15:03:06.693008 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.34299e-05 (* 0.0454545 = 6.10449e-07 loss) | |
| I0404 15:03:06.693024 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.41079e-05 (* 0.0454545 = 6.41269e-07 loss) | |
| I0404 15:03:06.693038 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.30238e-05 (* 0.0454545 = 5.91992e-07 loss) | |
| I0404 15:03:06.693053 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.27407e-05 (* 0.0454545 = 5.79122e-07 loss) | |
| I0404 15:03:06.693066 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.35528e-05 (* 0.0454545 = 6.16038e-07 loss) | |
| I0404 15:03:06.693078 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:03:06.693089 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000447123 | |
| I0404 15:03:06.693104 9252 sgd_solver.cpp:106] Iteration 56500, lr = 0.009435 | |
| I0404 15:04:17.465378 9252 solver.cpp:229] Iteration 57000, loss = 0.839226 | |
| I0404 15:04:17.465524 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.25 | |
| I0404 15:04:17.465544 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.1875 | |
| I0404 15:04:17.465558 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 15:04:17.465569 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.28125 | |
| I0404 15:04:17.465581 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.4375 | |
| I0404 15:04:17.465592 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.625 | |
| I0404 15:04:17.465605 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 15:04:17.465616 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:04:17.465628 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:04:17.465641 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:04:17.465652 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:04:17.465663 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:04:17.465675 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:04:17.465687 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:04:17.465698 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:04:17.465710 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:04:17.465723 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:04:17.465734 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:04:17.465747 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:04:17.465759 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:04:17.465772 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:04:17.465785 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:04:17.465801 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.59708 (* 0.0454545 = 0.118049 loss) | |
| I0404 15:04:17.465822 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.97349 (* 0.0454545 = 0.135159 loss) | |
| I0404 15:04:17.465837 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.04657 (* 0.0454545 = 0.13848 loss) | |
| I0404 15:04:17.465850 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.69955 (* 0.0454545 = 0.122707 loss) | |
| I0404 15:04:17.465863 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.10701 (* 0.0454545 = 0.0957734 loss) | |
| I0404 15:04:17.465878 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.66098 (* 0.0454545 = 0.075499 loss) | |
| I0404 15:04:17.465896 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.883897 (* 0.0454545 = 0.0401771 loss) | |
| I0404 15:04:17.465909 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.490571 (* 0.0454545 = 0.0222987 loss) | |
| I0404 15:04:17.465924 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.334139 (* 0.0454545 = 0.0151881 loss) | |
| I0404 15:04:17.465937 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0402396 (* 0.0454545 = 0.00182907 loss) | |
| I0404 15:04:17.465952 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.46769e-05 (* 0.0454545 = 1.57622e-06 loss) | |
| I0404 15:04:17.465966 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.29576e-05 (* 0.0454545 = 1.49807e-06 loss) | |
| I0404 15:04:17.465981 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.99081e-05 (* 0.0454545 = 1.35946e-06 loss) | |
| I0404 15:04:17.465994 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.25495e-05 (* 0.0454545 = 1.47952e-06 loss) | |
| I0404 15:04:17.466012 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.10612e-05 (* 0.0454545 = 1.41187e-06 loss) | |
| I0404 15:04:17.466027 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.46211e-05 (* 0.0454545 = 1.57369e-06 loss) | |
| I0404 15:04:17.466040 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.12697e-05 (* 0.0454545 = 1.42135e-06 loss) | |
| I0404 15:04:17.466080 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.0208e-05 (* 0.0454545 = 1.37309e-06 loss) | |
| I0404 15:04:17.466095 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.2112e-05 (* 0.0454545 = 1.45964e-06 loss) | |
| I0404 15:04:17.466109 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.16723e-05 (* 0.0454545 = 1.43965e-06 loss) | |
| I0404 15:04:17.466131 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.77492e-05 (* 0.0454545 = 1.26133e-06 loss) | |
| I0404 15:04:17.466145 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.69126e-05 (* 0.0454545 = 1.2233e-06 loss) | |
| I0404 15:04:17.466157 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 15:04:17.466169 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00348629 | |
| I0404 15:04:17.466184 9252 sgd_solver.cpp:106] Iteration 57000, lr = 0.00943 | |
| I0404 15:05:28.165192 9252 solver.cpp:229] Iteration 57500, loss = 0.831928 | |
| I0404 15:05:28.165334 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.4375 | |
| I0404 15:05:28.165354 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 15:05:28.165366 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.25 | |
| I0404 15:05:28.165379 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 15:05:28.165390 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 15:05:28.165402 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 15:05:28.165415 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 15:05:28.165426 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 15:05:28.165437 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:05:28.165449 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:05:28.165474 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:05:28.165489 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:05:28.165510 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:05:28.165525 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:05:28.165537 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:05:28.165565 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:05:28.165577 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:05:28.165590 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:05:28.165601 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:05:28.165612 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:05:28.165630 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:05:28.165642 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:05:28.165658 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.95218 (* 0.0454545 = 0.0887356 loss) | |
| I0404 15:05:28.165673 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.92385 (* 0.0454545 = 0.132902 loss) | |
| I0404 15:05:28.165686 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.78438 (* 0.0454545 = 0.126563 loss) | |
| I0404 15:05:28.165700 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.95459 (* 0.0454545 = 0.1343 loss) | |
| I0404 15:05:28.165714 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.58335 (* 0.0454545 = 0.117425 loss) | |
| I0404 15:05:28.165727 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.80601 (* 0.0454545 = 0.0820912 loss) | |
| I0404 15:05:28.165741 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.758131 (* 0.0454545 = 0.0344605 loss) | |
| I0404 15:05:28.165758 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.307435 (* 0.0454545 = 0.0139743 loss) | |
| I0404 15:05:28.165772 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.118097 (* 0.0454545 = 0.00536805 loss) | |
| I0404 15:05:28.165787 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.134458 (* 0.0454545 = 0.00611172 loss) | |
| I0404 15:05:28.165802 9252 solver.cpp:245] Train net output #32: loss/loss11 = 5.67295e-05 (* 0.0454545 = 2.57861e-06 loss) | |
| I0404 15:05:28.165815 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.03512e-05 (* 0.0454545 = 2.28869e-06 loss) | |
| I0404 15:05:28.165830 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.40099e-05 (* 0.0454545 = 2.455e-06 loss) | |
| I0404 15:05:28.165844 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.32853e-05 (* 0.0454545 = 2.42206e-06 loss) | |
| I0404 15:05:28.165858 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.46859e-05 (* 0.0454545 = 2.48572e-06 loss) | |
| I0404 15:05:28.165873 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.41662e-05 (* 0.0454545 = 2.4621e-06 loss) | |
| I0404 15:05:28.165886 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.35161e-05 (* 0.0454545 = 2.43255e-06 loss) | |
| I0404 15:05:28.165920 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.27652e-05 (* 0.0454545 = 2.39842e-06 loss) | |
| I0404 15:05:28.165935 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.99375e-05 (* 0.0454545 = 2.26989e-06 loss) | |
| I0404 15:05:28.165949 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.51928e-05 (* 0.0454545 = 2.50876e-06 loss) | |
| I0404 15:05:28.165962 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.22289e-05 (* 0.0454545 = 2.37404e-06 loss) | |
| I0404 15:05:28.165977 9252 solver.cpp:245] Train net output #43: loss/loss22 = 5.21506e-05 (* 0.0454545 = 2.37048e-06 loss) | |
| I0404 15:05:28.165988 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:05:28.166000 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000728259 | |
| I0404 15:05:28.166013 9252 sgd_solver.cpp:106] Iteration 57500, lr = 0.009425 | |
| I0404 15:06:39.483847 9252 solver.cpp:229] Iteration 58000, loss = 0.830155 | |
| I0404 15:06:39.484094 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.1875 | |
| I0404 15:06:39.484115 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 15:06:39.484128 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 15:06:39.484140 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 15:06:39.484153 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.375 | |
| I0404 15:06:39.484165 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 15:06:39.484187 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 15:06:39.484210 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:06:39.484225 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 15:06:39.484236 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:06:39.484254 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:06:39.484266 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:06:39.484277 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:06:39.484288 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:06:39.484300 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:06:39.484315 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:06:39.484328 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:06:39.484338 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:06:39.484349 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:06:39.484361 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:06:39.484381 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:06:39.484392 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:06:39.484408 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.73754 (* 0.0454545 = 0.124434 loss) | |
| I0404 15:06:39.484422 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.90789 (* 0.0454545 = 0.132177 loss) | |
| I0404 15:06:39.484436 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.04719 (* 0.0454545 = 0.138509 loss) | |
| I0404 15:06:39.484458 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.88111 (* 0.0454545 = 0.13096 loss) | |
| I0404 15:06:39.484472 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.36588 (* 0.0454545 = 0.10754 loss) | |
| I0404 15:06:39.484485 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.4658 (* 0.0454545 = 0.112082 loss) | |
| I0404 15:06:39.484499 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.36499 (* 0.0454545 = 0.0620451 loss) | |
| I0404 15:06:39.484513 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.530545 (* 0.0454545 = 0.0241157 loss) | |
| I0404 15:06:39.484526 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.40808 (* 0.0454545 = 0.0185491 loss) | |
| I0404 15:06:39.484540 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.242333 (* 0.0454545 = 0.0110152 loss) | |
| I0404 15:06:39.484555 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.66269e-05 (* 0.0454545 = 1.66486e-06 loss) | |
| I0404 15:06:39.484570 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.22437e-05 (* 0.0454545 = 1.46562e-06 loss) | |
| I0404 15:06:39.484593 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.55951e-05 (* 0.0454545 = 1.61796e-06 loss) | |
| I0404 15:06:39.484619 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.41385e-05 (* 0.0454545 = 1.55175e-06 loss) | |
| I0404 15:06:39.484635 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.18823e-05 (* 0.0454545 = 1.44919e-06 loss) | |
| I0404 15:06:39.484649 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.76294e-05 (* 0.0454545 = 1.71043e-06 loss) | |
| I0404 15:06:39.484663 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.15526e-05 (* 0.0454545 = 1.43421e-06 loss) | |
| I0404 15:06:39.484699 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.34526e-05 (* 0.0454545 = 1.52057e-06 loss) | |
| I0404 15:06:39.484715 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.24468e-05 (* 0.0454545 = 1.47485e-06 loss) | |
| I0404 15:06:39.484730 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.22959e-05 (* 0.0454545 = 1.468e-06 loss) | |
| I0404 15:06:39.484743 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.25736e-05 (* 0.0454545 = 1.48062e-06 loss) | |
| I0404 15:06:39.484757 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.37228e-05 (* 0.0454545 = 1.53286e-06 loss) | |
| I0404 15:06:39.484769 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:06:39.484781 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000443866 | |
| I0404 15:06:39.484797 9252 sgd_solver.cpp:106] Iteration 58000, lr = 0.00942 | |
| I0404 15:07:50.460594 9252 solver.cpp:229] Iteration 58500, loss = 0.833686 | |
| I0404 15:07:50.460813 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.21875 | |
| I0404 15:07:50.460842 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 15:07:50.460857 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 15:07:50.460877 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 15:07:50.460888 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 15:07:50.460901 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 15:07:50.460916 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 15:07:50.460944 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:07:50.460958 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:07:50.460969 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:07:50.460981 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:07:50.460994 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:07:50.461004 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:07:50.461015 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:07:50.461027 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:07:50.461038 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:07:50.461050 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:07:50.461061 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:07:50.461072 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:07:50.461083 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:07:50.461094 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:07:50.461107 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:07:50.461122 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.48683 (* 0.0454545 = 0.113038 loss) | |
| I0404 15:07:50.461136 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.95377 (* 0.0454545 = 0.134262 loss) | |
| I0404 15:07:50.461150 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.18069 (* 0.0454545 = 0.144577 loss) | |
| I0404 15:07:50.461163 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.11375 (* 0.0454545 = 0.141534 loss) | |
| I0404 15:07:50.461176 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.7614 (* 0.0454545 = 0.125518 loss) | |
| I0404 15:07:50.461190 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.24909 (* 0.0454545 = 0.102232 loss) | |
| I0404 15:07:50.461205 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.778603 (* 0.0454545 = 0.0353911 loss) | |
| I0404 15:07:50.461217 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.552612 (* 0.0454545 = 0.0251187 loss) | |
| I0404 15:07:50.461231 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.176374 (* 0.0454545 = 0.00801698 loss) | |
| I0404 15:07:50.461246 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.171954 (* 0.0454545 = 0.00781607 loss) | |
| I0404 15:07:50.461261 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.06073e-05 (* 0.0454545 = 9.36693e-07 loss) | |
| I0404 15:07:50.461273 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.8387e-05 (* 0.0454545 = 8.35772e-07 loss) | |
| I0404 15:07:50.461287 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.71667e-05 (* 0.0454545 = 7.80303e-07 loss) | |
| I0404 15:07:50.461302 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.75466e-05 (* 0.0454545 = 7.97574e-07 loss) | |
| I0404 15:07:50.461315 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.66041e-05 (* 0.0454545 = 7.54733e-07 loss) | |
| I0404 15:07:50.461329 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.79042e-05 (* 0.0454545 = 8.13827e-07 loss) | |
| I0404 15:07:50.461343 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.81465e-05 (* 0.0454545 = 8.24839e-07 loss) | |
| I0404 15:07:50.461391 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.9115e-05 (* 0.0454545 = 8.68863e-07 loss) | |
| I0404 15:07:50.461467 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.64215e-05 (* 0.0454545 = 7.46432e-07 loss) | |
| I0404 15:07:50.461499 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.65817e-05 (* 0.0454545 = 7.53711e-07 loss) | |
| I0404 15:07:50.461518 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.55832e-05 (* 0.0454545 = 7.08329e-07 loss) | |
| I0404 15:07:50.461531 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.6116e-05 (* 0.0454545 = 7.32547e-07 loss) | |
| I0404 15:07:50.461544 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:07:50.461555 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00114636 | |
| I0404 15:07:50.461570 9252 sgd_solver.cpp:106] Iteration 58500, lr = 0.009415 | |
| I0404 15:09:02.760632 9252 solver.cpp:229] Iteration 59000, loss = 0.826913 | |
| I0404 15:09:02.760759 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.46875 | |
| I0404 15:09:02.760778 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 15:09:02.760792 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.25 | |
| I0404 15:09:02.760804 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.25 | |
| I0404 15:09:02.760817 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 15:09:02.760828 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.625 | |
| I0404 15:09:02.760839 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:09:02.760851 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:09:02.760864 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:09:02.760876 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:09:02.760888 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:09:02.760900 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:09:02.760911 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:09:02.760923 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:09:02.760934 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:09:02.760946 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:09:02.760957 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:09:02.760969 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:09:02.760980 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:09:02.760993 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:09:02.761003 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:09:02.761015 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:09:02.761039 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.03075 (* 0.0454545 = 0.0923067 loss) | |
| I0404 15:09:02.761052 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.75039 (* 0.0454545 = 0.125018 loss) | |
| I0404 15:09:02.761066 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.72426 (* 0.0454545 = 0.12383 loss) | |
| I0404 15:09:02.761080 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.90886 (* 0.0454545 = 0.132221 loss) | |
| I0404 15:09:02.761098 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.51408 (* 0.0454545 = 0.114277 loss) | |
| I0404 15:09:02.761111 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.66259 (* 0.0454545 = 0.0755725 loss) | |
| I0404 15:09:02.761126 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.13162 (* 0.0454545 = 0.0514373 loss) | |
| I0404 15:09:02.761139 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.49688 (* 0.0454545 = 0.0225854 loss) | |
| I0404 15:09:02.761153 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.183049 (* 0.0454545 = 0.00832042 loss) | |
| I0404 15:09:02.761168 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.163544 (* 0.0454545 = 0.00743382 loss) | |
| I0404 15:09:02.761183 9252 solver.cpp:245] Train net output #32: loss/loss11 = 5.48831e-05 (* 0.0454545 = 2.49469e-06 loss) | |
| I0404 15:09:02.761196 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.40564e-05 (* 0.0454545 = 2.45711e-06 loss) | |
| I0404 15:09:02.761210 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.39198e-05 (* 0.0454545 = 2.4509e-06 loss) | |
| I0404 15:09:02.761224 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.08744e-05 (* 0.0454545 = 2.31247e-06 loss) | |
| I0404 15:09:02.761239 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.56061e-05 (* 0.0454545 = 2.52755e-06 loss) | |
| I0404 15:09:02.761253 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.48562e-05 (* 0.0454545 = 2.49347e-06 loss) | |
| I0404 15:09:02.761266 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.94715e-05 (* 0.0454545 = 2.24871e-06 loss) | |
| I0404 15:09:02.761299 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.47826e-05 (* 0.0454545 = 2.49012e-06 loss) | |
| I0404 15:09:02.761314 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.79272e-05 (* 0.0454545 = 2.17851e-06 loss) | |
| I0404 15:09:02.761328 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.53173e-05 (* 0.0454545 = 2.05988e-06 loss) | |
| I0404 15:09:02.761343 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.05502e-05 (* 0.0454545 = 2.29774e-06 loss) | |
| I0404 15:09:02.761356 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.90244e-05 (* 0.0454545 = 2.22838e-06 loss) | |
| I0404 15:09:02.761368 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:09:02.761380 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00133815 | |
| I0404 15:09:02.761394 9252 sgd_solver.cpp:106] Iteration 59000, lr = 0.00941 | |
| I0404 15:10:14.221436 9252 solver.cpp:229] Iteration 59500, loss = 0.823798 | |
| I0404 15:10:14.221561 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.34375 | |
| I0404 15:10:14.221590 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 15:10:14.221616 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 15:10:14.221638 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 15:10:14.221659 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 15:10:14.221678 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 15:10:14.221695 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 15:10:14.221717 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 15:10:14.221740 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:10:14.221767 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:10:14.221791 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:10:14.221812 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:10:14.221843 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:10:14.221864 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:10:14.221885 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:10:14.221916 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:10:14.221938 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:10:14.221958 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:10:14.221979 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:10:14.222000 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:10:14.222023 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:10:14.222044 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:10:14.222071 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.54578 (* 0.0454545 = 0.115717 loss) | |
| I0404 15:10:14.222105 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.96846 (* 0.0454545 = 0.13493 loss) | |
| I0404 15:10:14.222131 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.25023 (* 0.0454545 = 0.147738 loss) | |
| I0404 15:10:14.222157 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.2563 (* 0.0454545 = 0.148014 loss) | |
| I0404 15:10:14.222182 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.67237 (* 0.0454545 = 0.121471 loss) | |
| I0404 15:10:14.222208 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.3107 (* 0.0454545 = 0.105032 loss) | |
| I0404 15:10:14.222232 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.22187 (* 0.0454545 = 0.0555396 loss) | |
| I0404 15:10:14.222259 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.451497 (* 0.0454545 = 0.0205226 loss) | |
| I0404 15:10:14.222290 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.12854 (* 0.0454545 = 0.00584271 loss) | |
| I0404 15:10:14.222316 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0373318 (* 0.0454545 = 0.0016969 loss) | |
| I0404 15:10:14.222342 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.03444e-05 (* 0.0454545 = 9.24745e-07 loss) | |
| I0404 15:10:14.222371 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.81017e-05 (* 0.0454545 = 8.22804e-07 loss) | |
| I0404 15:10:14.222403 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.97186e-05 (* 0.0454545 = 8.96298e-07 loss) | |
| I0404 15:10:14.222430 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.7934e-05 (* 0.0454545 = 8.15183e-07 loss) | |
| I0404 15:10:14.222458 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.9074e-05 (* 0.0454545 = 8.67e-07 loss) | |
| I0404 15:10:14.222492 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.82768e-05 (* 0.0454545 = 8.30762e-07 loss) | |
| I0404 15:10:14.222519 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.81538e-05 (* 0.0454545 = 8.25173e-07 loss) | |
| I0404 15:10:14.222575 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.87312e-05 (* 0.0454545 = 8.51419e-07 loss) | |
| I0404 15:10:14.222605 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.64289e-05 (* 0.0454545 = 7.46769e-07 loss) | |
| I0404 15:10:14.222632 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.66226e-05 (* 0.0454545 = 7.55574e-07 loss) | |
| I0404 15:10:14.222658 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.68723e-05 (* 0.0454545 = 7.66921e-07 loss) | |
| I0404 15:10:14.222686 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.84407e-05 (* 0.0454545 = 8.38213e-07 loss) | |
| I0404 15:10:14.222707 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:10:14.222729 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000347022 | |
| I0404 15:10:14.222754 9252 sgd_solver.cpp:106] Iteration 59500, lr = 0.009405 | |
| I0404 15:11:24.887190 9252 solver.cpp:338] Iteration 60000, Testing net (#0) | |
| I0404 15:11:32.989130 9252 solver.cpp:393] Test loss: 0.728368 | |
| I0404 15:11:32.989179 9252 solver.cpp:406] Test net output #0: loss/accuracy01 = 0.38 | |
| I0404 15:11:32.989195 9252 solver.cpp:406] Test net output #1: loss/accuracy02 = 0.159 | |
| I0404 15:11:32.989207 9252 solver.cpp:406] Test net output #2: loss/accuracy03 = 0.141 | |
| I0404 15:11:32.989219 9252 solver.cpp:406] Test net output #3: loss/accuracy04 = 0.176 | |
| I0404 15:11:32.989230 9252 solver.cpp:406] Test net output #4: loss/accuracy05 = 0.282 | |
| I0404 15:11:32.989241 9252 solver.cpp:406] Test net output #5: loss/accuracy06 = 0.521 | |
| I0404 15:11:32.989253 9252 solver.cpp:406] Test net output #6: loss/accuracy07 = 0.892 | |
| I0404 15:11:32.989264 9252 solver.cpp:406] Test net output #7: loss/accuracy08 = 0.97 | |
| I0404 15:11:32.989275 9252 solver.cpp:406] Test net output #8: loss/accuracy09 = 0.995 | |
| I0404 15:11:32.989286 9252 solver.cpp:406] Test net output #9: loss/accuracy10 = 0.998 | |
| I0404 15:11:32.989297 9252 solver.cpp:406] Test net output #10: loss/accuracy11 = 1 | |
| I0404 15:11:32.989308 9252 solver.cpp:406] Test net output #11: loss/accuracy12 = 1 | |
| I0404 15:11:32.989320 9252 solver.cpp:406] Test net output #12: loss/accuracy13 = 1 | |
| I0404 15:11:32.989331 9252 solver.cpp:406] Test net output #13: loss/accuracy14 = 1 | |
| I0404 15:11:32.989341 9252 solver.cpp:406] Test net output #14: loss/accuracy15 = 1 | |
| I0404 15:11:32.989352 9252 solver.cpp:406] Test net output #15: loss/accuracy16 = 1 | |
| I0404 15:11:32.989363 9252 solver.cpp:406] Test net output #16: loss/accuracy17 = 1 | |
| I0404 15:11:32.989374 9252 solver.cpp:406] Test net output #17: loss/accuracy18 = 1 | |
| I0404 15:11:32.989387 9252 solver.cpp:406] Test net output #18: loss/accuracy19 = 1 | |
| I0404 15:11:32.989398 9252 solver.cpp:406] Test net output #19: loss/accuracy20 = 1 | |
| I0404 15:11:32.989408 9252 solver.cpp:406] Test net output #20: loss/accuracy21 = 1 | |
| I0404 15:11:32.989437 9252 solver.cpp:406] Test net output #21: loss/accuracy22 = 1 | |
| I0404 15:11:32.989455 9252 solver.cpp:406] Test net output #22: loss/loss01 = 2.24305 (* 0.0454545 = 0.101957 loss) | |
| I0404 15:11:32.989470 9252 solver.cpp:406] Test net output #23: loss/loss02 = 2.78515 (* 0.0454545 = 0.126598 loss) | |
| I0404 15:11:32.989483 9252 solver.cpp:406] Test net output #24: loss/loss03 = 2.90429 (* 0.0454545 = 0.132013 loss) | |
| I0404 15:11:32.989496 9252 solver.cpp:406] Test net output #25: loss/loss04 = 2.84632 (* 0.0454545 = 0.129378 loss) | |
| I0404 15:11:32.989509 9252 solver.cpp:406] Test net output #26: loss/loss05 = 2.55519 (* 0.0454545 = 0.116145 loss) | |
| I0404 15:11:32.989523 9252 solver.cpp:406] Test net output #27: loss/loss06 = 1.88654 (* 0.0454545 = 0.0857517 loss) | |
| I0404 15:11:32.989537 9252 solver.cpp:406] Test net output #28: loss/loss07 = 0.525351 (* 0.0454545 = 0.0238796 loss) | |
| I0404 15:11:32.989550 9252 solver.cpp:406] Test net output #29: loss/loss08 = 0.203578 (* 0.0454545 = 0.00925355 loss) | |
| I0404 15:11:32.989564 9252 solver.cpp:406] Test net output #30: loss/loss09 = 0.0482533 (* 0.0454545 = 0.00219333 loss) | |
| I0404 15:11:32.989578 9252 solver.cpp:406] Test net output #31: loss/loss10 = 0.0254176 (* 0.0454545 = 0.00115534 loss) | |
| I0404 15:11:32.989591 9252 solver.cpp:406] Test net output #32: loss/loss11 = 8.82109e-05 (* 0.0454545 = 4.00958e-06 loss) | |
| I0404 15:11:32.989605 9252 solver.cpp:406] Test net output #33: loss/loss12 = 7.66382e-05 (* 0.0454545 = 3.48356e-06 loss) | |
| I0404 15:11:32.989619 9252 solver.cpp:406] Test net output #34: loss/loss13 = 8.17979e-05 (* 0.0454545 = 3.71809e-06 loss) | |
| I0404 15:11:32.989632 9252 solver.cpp:406] Test net output #35: loss/loss14 = 7.99719e-05 (* 0.0454545 = 3.63509e-06 loss) | |
| I0404 15:11:32.989646 9252 solver.cpp:406] Test net output #36: loss/loss15 = 8.39315e-05 (* 0.0454545 = 3.81507e-06 loss) | |
| I0404 15:11:32.989660 9252 solver.cpp:406] Test net output #37: loss/loss16 = 8.43388e-05 (* 0.0454545 = 3.83358e-06 loss) | |
| I0404 15:11:32.989673 9252 solver.cpp:406] Test net output #38: loss/loss17 = 7.1345e-05 (* 0.0454545 = 3.24296e-06 loss) | |
| I0404 15:11:32.989725 9252 solver.cpp:406] Test net output #39: loss/loss18 = 8.58024e-05 (* 0.0454545 = 3.90011e-06 loss) | |
| I0404 15:11:32.989742 9252 solver.cpp:406] Test net output #40: loss/loss19 = 8.62968e-05 (* 0.0454545 = 3.92258e-06 loss) | |
| I0404 15:11:32.989758 9252 solver.cpp:406] Test net output #41: loss/loss20 = 8.17133e-05 (* 0.0454545 = 3.71424e-06 loss) | |
| I0404 15:11:32.989775 9252 solver.cpp:406] Test net output #42: loss/loss21 = 8.10353e-05 (* 0.0454545 = 3.68342e-06 loss) | |
| I0404 15:11:32.989789 9252 solver.cpp:406] Test net output #43: loss/loss22 = 7.1701e-05 (* 0.0454545 = 3.25914e-06 loss) | |
| I0404 15:11:32.989801 9252 solver.cpp:406] Test net output #44: total_accuracy = 0.001 | |
| I0404 15:11:32.989812 9252 solver.cpp:406] Test net output #45: total_confidence = 0.000954465 | |
| I0404 15:11:33.025135 9252 solver.cpp:229] Iteration 60000, loss = 0.82471 | |
| I0404 15:11:33.025177 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.4375 | |
| I0404 15:11:33.025193 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 15:11:33.025205 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 15:11:33.025218 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.28125 | |
| I0404 15:11:33.025229 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 15:11:33.025240 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 15:11:33.025252 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 15:11:33.025264 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 15:11:33.025275 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.875 | |
| I0404 15:11:33.025287 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 15:11:33.025302 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:11:33.025315 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:11:33.025326 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:11:33.025337 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:11:33.025348 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:11:33.025359 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:11:33.025370 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:11:33.025382 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:11:33.025393 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:11:33.025405 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:11:33.025430 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:11:33.025446 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:11:33.025461 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.26772 (* 0.0454545 = 0.103078 loss) | |
| I0404 15:11:33.025476 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.75548 (* 0.0454545 = 0.125249 loss) | |
| I0404 15:11:33.025490 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.88399 (* 0.0454545 = 0.131091 loss) | |
| I0404 15:11:33.025504 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.98003 (* 0.0454545 = 0.135456 loss) | |
| I0404 15:11:33.025518 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.81476 (* 0.0454545 = 0.127944 loss) | |
| I0404 15:11:33.025532 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.18596 (* 0.0454545 = 0.0993616 loss) | |
| I0404 15:11:33.025545 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.04698 (* 0.0454545 = 0.04759 loss) | |
| I0404 15:11:33.025558 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.485566 (* 0.0454545 = 0.0220712 loss) | |
| I0404 15:11:33.025573 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.451106 (* 0.0454545 = 0.0205048 loss) | |
| I0404 15:11:33.025605 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.310016 (* 0.0454545 = 0.0140916 loss) | |
| I0404 15:11:33.025620 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.14051e-05 (* 0.0454545 = 2.79114e-06 loss) | |
| I0404 15:11:33.025635 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.64876e-05 (* 0.0454545 = 2.11307e-06 loss) | |
| I0404 15:11:33.025648 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.67435e-05 (* 0.0454545 = 2.57925e-06 loss) | |
| I0404 15:11:33.025661 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.56684e-05 (* 0.0454545 = 2.53038e-06 loss) | |
| I0404 15:11:33.025676 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.63705e-05 (* 0.0454545 = 2.10775e-06 loss) | |
| I0404 15:11:33.025688 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.84082e-05 (* 0.0454545 = 2.65492e-06 loss) | |
| I0404 15:11:33.025702 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.15804e-05 (* 0.0454545 = 2.34456e-06 loss) | |
| I0404 15:11:33.025715 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.12135e-05 (* 0.0454545 = 2.32789e-06 loss) | |
| I0404 15:11:33.025729 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.25491e-05 (* 0.0454545 = 2.38859e-06 loss) | |
| I0404 15:11:33.025743 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.95364e-05 (* 0.0454545 = 2.25166e-06 loss) | |
| I0404 15:11:33.025756 9252 solver.cpp:245] Train net output #42: loss/loss21 = 5.04278e-05 (* 0.0454545 = 2.29217e-06 loss) | |
| I0404 15:11:33.025770 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.67565e-05 (* 0.0454545 = 2.1253e-06 loss) | |
| I0404 15:11:33.025782 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:11:33.025794 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000471519 | |
| I0404 15:11:33.025809 9252 sgd_solver.cpp:106] Iteration 60000, lr = 0.0094 | |
| I0404 15:12:44.415822 9252 solver.cpp:229] Iteration 60500, loss = 0.818026 | |
| I0404 15:12:44.416000 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.375 | |
| I0404 15:12:44.416039 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 15:12:44.416066 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 15:12:44.416090 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.03125 | |
| I0404 15:12:44.416112 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 15:12:44.416136 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 15:12:44.416159 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.59375 | |
| I0404 15:12:44.416185 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.8125 | |
| I0404 15:12:44.416208 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:12:44.416230 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:12:44.416271 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:12:44.416293 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:12:44.416322 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:12:44.416344 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:12:44.416365 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:12:44.416386 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:12:44.416407 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:12:44.416429 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:12:44.416450 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:12:44.416471 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:12:44.416492 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:12:44.416514 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:12:44.416541 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.11224 (* 0.0454545 = 0.096011 loss) | |
| I0404 15:12:44.416568 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.90972 (* 0.0454545 = 0.13226 loss) | |
| I0404 15:12:44.416596 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.94412 (* 0.0454545 = 0.133824 loss) | |
| I0404 15:12:44.416643 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.24221 (* 0.0454545 = 0.147373 loss) | |
| I0404 15:12:44.416674 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.48229 (* 0.0454545 = 0.112831 loss) | |
| I0404 15:12:44.416719 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.42921 (* 0.0454545 = 0.110419 loss) | |
| I0404 15:12:44.416749 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.49016 (* 0.0454545 = 0.0677345 loss) | |
| I0404 15:12:44.416785 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.603651 (* 0.0454545 = 0.0274387 loss) | |
| I0404 15:12:44.416811 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.449817 (* 0.0454545 = 0.0204462 loss) | |
| I0404 15:12:44.416837 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.210961 (* 0.0454545 = 0.00958915 loss) | |
| I0404 15:12:44.416864 9252 solver.cpp:245] Train net output #32: loss/loss11 = 8.69932e-05 (* 0.0454545 = 3.95424e-06 loss) | |
| I0404 15:12:44.416890 9252 solver.cpp:245] Train net output #33: loss/loss12 = 8.17242e-05 (* 0.0454545 = 3.71474e-06 loss) | |
| I0404 15:12:44.416916 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.96633e-05 (* 0.0454545 = 3.62106e-06 loss) | |
| I0404 15:12:44.416942 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.17113e-05 (* 0.0454545 = 3.2596e-06 loss) | |
| I0404 15:12:44.416970 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.14502e-05 (* 0.0454545 = 3.24773e-06 loss) | |
| I0404 15:12:44.417002 9252 solver.cpp:245] Train net output #37: loss/loss16 = 7.29714e-05 (* 0.0454545 = 3.31688e-06 loss) | |
| I0404 15:12:44.417028 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.52083e-05 (* 0.0454545 = 3.41856e-06 loss) | |
| I0404 15:12:44.417083 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.47257e-05 (* 0.0454545 = 3.39662e-06 loss) | |
| I0404 15:12:44.417111 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.89156e-05 (* 0.0454545 = 3.13253e-06 loss) | |
| I0404 15:12:44.417138 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.24512e-05 (* 0.0454545 = 2.83869e-06 loss) | |
| I0404 15:12:44.417165 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.60896e-05 (* 0.0454545 = 3.00407e-06 loss) | |
| I0404 15:12:44.417191 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.7184e-05 (* 0.0454545 = 3.05382e-06 loss) | |
| I0404 15:12:44.417214 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:12:44.417235 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000453201 | |
| I0404 15:12:44.417258 9252 sgd_solver.cpp:106] Iteration 60500, lr = 0.009395 | |
| I0404 15:13:55.623414 9252 solver.cpp:229] Iteration 61000, loss = 0.814955 | |
| I0404 15:13:55.623515 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.21875 | |
| I0404 15:13:55.623533 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 15:13:55.623546 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.0625 | |
| I0404 15:13:55.623558 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 15:13:55.623570 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.4375 | |
| I0404 15:13:55.623582 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 15:13:55.623594 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.875 | |
| I0404 15:13:55.623605 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 15:13:55.623618 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 15:13:55.623639 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:13:55.623663 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:13:55.623678 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:13:55.623690 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:13:55.623702 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:13:55.623713 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:13:55.623725 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:13:55.623736 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:13:55.623747 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:13:55.623759 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:13:55.623770 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:13:55.623782 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:13:55.623793 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:13:55.623810 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.42415 (* 0.0454545 = 0.110188 loss) | |
| I0404 15:13:55.623823 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.89196 (* 0.0454545 = 0.131453 loss) | |
| I0404 15:13:55.623837 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.94177 (* 0.0454545 = 0.133717 loss) | |
| I0404 15:13:55.623850 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.17082 (* 0.0454545 = 0.144128 loss) | |
| I0404 15:13:55.623864 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.35765 (* 0.0454545 = 0.107166 loss) | |
| I0404 15:13:55.623878 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.14482 (* 0.0454545 = 0.097492 loss) | |
| I0404 15:13:55.623893 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.446864 (* 0.0454545 = 0.020312 loss) | |
| I0404 15:13:55.623910 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.19832 (* 0.0454545 = 0.00901455 loss) | |
| I0404 15:13:55.623924 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0105511 (* 0.0454545 = 0.000479594 loss) | |
| I0404 15:13:55.623939 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00184753 (* 0.0454545 = 8.39785e-05 loss) | |
| I0404 15:13:55.623953 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.02001e-05 (* 0.0454545 = 4.63642e-07 loss) | |
| I0404 15:13:55.623967 9252 solver.cpp:245] Train net output #33: loss/loss12 = 9.43644e-06 (* 0.0454545 = 4.28929e-07 loss) | |
| I0404 15:13:55.623981 9252 solver.cpp:245] Train net output #34: loss/loss13 = 9.59659e-06 (* 0.0454545 = 4.36209e-07 loss) | |
| I0404 15:13:55.623996 9252 solver.cpp:245] Train net output #35: loss/loss14 = 9.54446e-06 (* 0.0454545 = 4.33839e-07 loss) | |
| I0404 15:13:55.624009 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.04907e-05 (* 0.0454545 = 4.7685e-07 loss) | |
| I0404 15:13:55.624023 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.08819e-05 (* 0.0454545 = 4.94633e-07 loss) | |
| I0404 15:13:55.624037 9252 solver.cpp:245] Train net output #38: loss/loss17 = 9.38797e-06 (* 0.0454545 = 4.26726e-07 loss) | |
| I0404 15:13:55.624068 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.03827e-05 (* 0.0454545 = 4.7194e-07 loss) | |
| I0404 15:13:55.624083 9252 solver.cpp:245] Train net output #40: loss/loss19 = 9.32468e-06 (* 0.0454545 = 4.23849e-07 loss) | |
| I0404 15:13:55.624097 9252 solver.cpp:245] Train net output #41: loss/loss20 = 9.35816e-06 (* 0.0454545 = 4.25371e-07 loss) | |
| I0404 15:13:55.624110 9252 solver.cpp:245] Train net output #42: loss/loss21 = 9.17936e-06 (* 0.0454545 = 4.17244e-07 loss) | |
| I0404 15:13:55.624125 9252 solver.cpp:245] Train net output #43: loss/loss22 = 9.19795e-06 (* 0.0454545 = 4.18089e-07 loss) | |
| I0404 15:13:55.624136 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:13:55.624147 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000556393 | |
| I0404 15:13:55.624161 9252 sgd_solver.cpp:106] Iteration 61000, lr = 0.00939 | |
| I0404 15:15:06.930893 9252 solver.cpp:229] Iteration 61500, loss = 0.815831 | |
| I0404 15:15:06.931025 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.40625 | |
| I0404 15:15:06.931052 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.09375 | |
| I0404 15:15:06.931066 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 15:15:06.931078 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 15:15:06.931090 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 15:15:06.931102 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 15:15:06.931114 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 15:15:06.931126 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.78125 | |
| I0404 15:15:06.931138 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 15:15:06.931150 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:15:06.931162 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:15:06.931174 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:15:06.931185 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:15:06.931205 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:15:06.931216 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:15:06.931227 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:15:06.931239 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:15:06.931252 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:15:06.931263 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:15:06.931274 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:15:06.931285 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:15:06.931298 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:15:06.931313 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.20148 (* 0.0454545 = 0.100067 loss) | |
| I0404 15:15:06.931327 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.93229 (* 0.0454545 = 0.133286 loss) | |
| I0404 15:15:06.931341 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.02375 (* 0.0454545 = 0.137443 loss) | |
| I0404 15:15:06.931354 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.02333 (* 0.0454545 = 0.137424 loss) | |
| I0404 15:15:06.931368 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.09648 (* 0.0454545 = 0.140749 loss) | |
| I0404 15:15:06.931382 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.28698 (* 0.0454545 = 0.103954 loss) | |
| I0404 15:15:06.931396 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.15309 (* 0.0454545 = 0.0524132 loss) | |
| I0404 15:15:06.931409 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.680849 (* 0.0454545 = 0.0309477 loss) | |
| I0404 15:15:06.931430 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.42069 (* 0.0454545 = 0.0191223 loss) | |
| I0404 15:15:06.931444 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.028716 (* 0.0454545 = 0.00130527 loss) | |
| I0404 15:15:06.931458 9252 solver.cpp:245] Train net output #32: loss/loss11 = 9.91868e-05 (* 0.0454545 = 4.50849e-06 loss) | |
| I0404 15:15:06.931473 9252 solver.cpp:245] Train net output #33: loss/loss12 = 8.77305e-05 (* 0.0454545 = 3.98775e-06 loss) | |
| I0404 15:15:06.931488 9252 solver.cpp:245] Train net output #34: loss/loss13 = 9.37148e-05 (* 0.0454545 = 4.25976e-06 loss) | |
| I0404 15:15:06.931500 9252 solver.cpp:245] Train net output #35: loss/loss14 = 9.41184e-05 (* 0.0454545 = 4.27811e-06 loss) | |
| I0404 15:15:06.931514 9252 solver.cpp:245] Train net output #36: loss/loss15 = 8.08212e-05 (* 0.0454545 = 3.67369e-06 loss) | |
| I0404 15:15:06.931529 9252 solver.cpp:245] Train net output #37: loss/loss16 = 8.96515e-05 (* 0.0454545 = 4.07507e-06 loss) | |
| I0404 15:15:06.931542 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.95707e-05 (* 0.0454545 = 3.61685e-06 loss) | |
| I0404 15:15:06.931956 9252 solver.cpp:245] Train net output #39: loss/loss18 = 8.1777e-05 (* 0.0454545 = 3.71714e-06 loss) | |
| I0404 15:15:06.931973 9252 solver.cpp:245] Train net output #40: loss/loss19 = 8.75375e-05 (* 0.0454545 = 3.97898e-06 loss) | |
| I0404 15:15:06.931988 9252 solver.cpp:245] Train net output #41: loss/loss20 = 8.12322e-05 (* 0.0454545 = 3.69237e-06 loss) | |
| I0404 15:15:06.932003 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.67668e-05 (* 0.0454545 = 3.4894e-06 loss) | |
| I0404 15:15:06.932016 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.95532e-05 (* 0.0454545 = 3.16151e-06 loss) | |
| I0404 15:15:06.932029 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 15:15:06.932044 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00057572 | |
| I0404 15:15:06.932059 9252 sgd_solver.cpp:106] Iteration 61500, lr = 0.009385 | |
| I0404 15:16:18.505662 9252 solver.cpp:229] Iteration 62000, loss = 0.809086 | |
| I0404 15:16:18.505964 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.4375 | |
| I0404 15:16:18.505993 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 15:16:18.506014 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 15:16:18.506036 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 15:16:18.506057 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 15:16:18.506080 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 15:16:18.506103 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 15:16:18.506124 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 15:16:18.506146 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 15:16:18.506168 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:16:18.506189 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:16:18.506211 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:16:18.506230 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:16:18.506253 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:16:18.506275 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:16:18.506299 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:16:18.506321 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:16:18.506343 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:16:18.506364 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:16:18.506386 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:16:18.506417 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:16:18.506439 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:16:18.506466 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.12191 (* 0.0454545 = 0.0964503 loss) | |
| I0404 15:16:18.506494 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.9106 (* 0.0454545 = 0.1323 loss) | |
| I0404 15:16:18.506520 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.08373 (* 0.0454545 = 0.140169 loss) | |
| I0404 15:16:18.506546 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.92285 (* 0.0454545 = 0.132857 loss) | |
| I0404 15:16:18.506572 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.52403 (* 0.0454545 = 0.114729 loss) | |
| I0404 15:16:18.506597 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.02351 (* 0.0454545 = 0.0919777 loss) | |
| I0404 15:16:18.506623 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.967348 (* 0.0454545 = 0.0439703 loss) | |
| I0404 15:16:18.506649 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.281429 (* 0.0454545 = 0.0127922 loss) | |
| I0404 15:16:18.506675 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0501319 (* 0.0454545 = 0.00227872 loss) | |
| I0404 15:16:18.506701 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0137379 (* 0.0454545 = 0.000624449 loss) | |
| I0404 15:16:18.506727 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.57872e-05 (* 0.0454545 = 7.176e-07 loss) | |
| I0404 15:16:18.506759 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.39022e-05 (* 0.0454545 = 6.31916e-07 loss) | |
| I0404 15:16:18.506785 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.50681e-05 (* 0.0454545 = 6.84915e-07 loss) | |
| I0404 15:16:18.506811 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.37418e-05 (* 0.0454545 = 6.24629e-07 loss) | |
| I0404 15:16:18.506837 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.31529e-05 (* 0.0454545 = 5.97861e-07 loss) | |
| I0404 15:16:18.506863 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.91114e-05 (* 0.0454545 = 8.68702e-07 loss) | |
| I0404 15:16:18.506888 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.265e-05 (* 0.0454545 = 5.74998e-07 loss) | |
| I0404 15:16:18.506934 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.30076e-05 (* 0.0454545 = 5.91257e-07 loss) | |
| I0404 15:16:18.506963 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.65533e-05 (* 0.0454545 = 7.52421e-07 loss) | |
| I0404 15:16:18.506994 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.40175e-05 (* 0.0454545 = 6.37157e-07 loss) | |
| I0404 15:16:18.507020 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.22662e-05 (* 0.0454545 = 5.57556e-07 loss) | |
| I0404 15:16:18.507047 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.28922e-05 (* 0.0454545 = 5.86008e-07 loss) | |
| I0404 15:16:18.507069 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:16:18.507091 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00136327 | |
| I0404 15:16:18.507117 9252 sgd_solver.cpp:106] Iteration 62000, lr = 0.00938 | |
| I0404 15:17:29.573237 9252 solver.cpp:229] Iteration 62500, loss = 0.805615 | |
| I0404 15:17:29.573354 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.625 | |
| I0404 15:17:29.573374 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0404 15:17:29.573387 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 15:17:29.573400 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.0625 | |
| I0404 15:17:29.573411 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 15:17:29.573424 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 15:17:29.573436 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:17:29.573448 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 15:17:29.573462 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 15:17:29.573483 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:17:29.573499 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:17:29.573511 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:17:29.573524 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:17:29.573536 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:17:29.573547 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:17:29.573559 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:17:29.573571 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:17:29.573582 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:17:29.573595 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:17:29.573606 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:17:29.573617 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:17:29.573637 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:17:29.573652 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.79045 (* 0.0454545 = 0.0813841 loss) | |
| I0404 15:17:29.573668 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.95047 (* 0.0454545 = 0.134112 loss) | |
| I0404 15:17:29.573681 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.05097 (* 0.0454545 = 0.138681 loss) | |
| I0404 15:17:29.573694 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.21384 (* 0.0454545 = 0.146084 loss) | |
| I0404 15:17:29.573709 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.86175 (* 0.0454545 = 0.130079 loss) | |
| I0404 15:17:29.573722 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.17865 (* 0.0454545 = 0.0990296 loss) | |
| I0404 15:17:29.573736 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.966348 (* 0.0454545 = 0.0439249 loss) | |
| I0404 15:17:29.573752 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.379595 (* 0.0454545 = 0.0172543 loss) | |
| I0404 15:17:29.573766 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.536231 (* 0.0454545 = 0.0243741 loss) | |
| I0404 15:17:29.573781 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0111051 (* 0.0454545 = 0.000504779 loss) | |
| I0404 15:17:29.573796 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.05031e-05 (* 0.0454545 = 1.3865e-06 loss) | |
| I0404 15:17:29.573809 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.50031e-05 (* 0.0454545 = 1.13651e-06 loss) | |
| I0404 15:17:29.573823 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.46864e-05 (* 0.0454545 = 1.12211e-06 loss) | |
| I0404 15:17:29.573837 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.50107e-05 (* 0.0454545 = 1.13685e-06 loss) | |
| I0404 15:17:29.573850 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.85616e-05 (* 0.0454545 = 1.29826e-06 loss) | |
| I0404 15:17:29.573864 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.59945e-05 (* 0.0454545 = 1.18157e-06 loss) | |
| I0404 15:17:29.573879 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.64061e-05 (* 0.0454545 = 1.20028e-06 loss) | |
| I0404 15:17:29.573910 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.82505e-05 (* 0.0454545 = 1.28411e-06 loss) | |
| I0404 15:17:29.573925 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.44034e-05 (* 0.0454545 = 1.10925e-06 loss) | |
| I0404 15:17:29.573940 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.7116e-05 (* 0.0454545 = 1.23255e-06 loss) | |
| I0404 15:17:29.573953 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.77384e-05 (* 0.0454545 = 1.26084e-06 loss) | |
| I0404 15:17:29.573967 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.1944e-05 (* 0.0454545 = 9.97456e-07 loss) | |
| I0404 15:17:29.573979 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:17:29.573992 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000398671 | |
| I0404 15:17:29.574007 9252 sgd_solver.cpp:106] Iteration 62500, lr = 0.009375 | |
| I0404 15:18:40.418826 9252 solver.cpp:229] Iteration 63000, loss = 0.803152 | |
| I0404 15:18:40.418936 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.34375 | |
| I0404 15:18:40.418954 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 15:18:40.418967 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 15:18:40.418979 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.25 | |
| I0404 15:18:40.418992 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 15:18:40.419004 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 15:18:40.419015 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:18:40.419028 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 15:18:40.419040 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:18:40.419052 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:18:40.419064 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:18:40.419076 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:18:40.419087 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:18:40.419100 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:18:40.419111 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:18:40.419122 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:18:40.419133 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:18:40.419145 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:18:40.419157 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:18:40.419168 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:18:40.419180 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:18:40.419191 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:18:40.419206 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.36695 (* 0.0454545 = 0.107589 loss) | |
| I0404 15:18:40.419221 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.9197 (* 0.0454545 = 0.132714 loss) | |
| I0404 15:18:40.419235 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.34299 (* 0.0454545 = 0.151954 loss) | |
| I0404 15:18:40.419248 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.88789 (* 0.0454545 = 0.131268 loss) | |
| I0404 15:18:40.419262 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.07239 (* 0.0454545 = 0.139654 loss) | |
| I0404 15:18:40.419275 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.94757 (* 0.0454545 = 0.088526 loss) | |
| I0404 15:18:40.419288 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.13373 (* 0.0454545 = 0.0515331 loss) | |
| I0404 15:18:40.419302 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.669096 (* 0.0454545 = 0.0304135 loss) | |
| I0404 15:18:40.419317 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.372182 (* 0.0454545 = 0.0169174 loss) | |
| I0404 15:18:40.419330 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.214375 (* 0.0454545 = 0.0097443 loss) | |
| I0404 15:18:40.419344 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.77583e-05 (* 0.0454545 = 1.26174e-06 loss) | |
| I0404 15:18:40.419358 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.13017e-05 (* 0.0454545 = 1.42281e-06 loss) | |
| I0404 15:18:40.419373 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.96789e-05 (* 0.0454545 = 1.34904e-06 loss) | |
| I0404 15:18:40.419387 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.21269e-05 (* 0.0454545 = 1.00577e-06 loss) | |
| I0404 15:18:40.419400 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.00705e-05 (* 0.0454545 = 1.36684e-06 loss) | |
| I0404 15:18:40.419414 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.34474e-05 (* 0.0454545 = 1.06579e-06 loss) | |
| I0404 15:18:40.419428 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.25439e-05 (* 0.0454545 = 1.02472e-06 loss) | |
| I0404 15:18:40.419459 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.73601e-05 (* 0.0454545 = 1.24364e-06 loss) | |
| I0404 15:18:40.419478 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.57429e-05 (* 0.0454545 = 1.17013e-06 loss) | |
| I0404 15:18:40.419493 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.25031e-05 (* 0.0454545 = 1.02287e-06 loss) | |
| I0404 15:18:40.419507 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.12739e-05 (* 0.0454545 = 9.66994e-07 loss) | |
| I0404 15:18:40.419522 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.24475e-05 (* 0.0454545 = 1.02034e-06 loss) | |
| I0404 15:18:40.419533 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:18:40.419544 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0012117 | |
| I0404 15:18:40.419558 9252 sgd_solver.cpp:106] Iteration 63000, lr = 0.00937 | |
| I0404 15:19:51.277770 9252 solver.cpp:229] Iteration 63500, loss = 0.802404 | |
| I0404 15:19:51.277875 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.4375 | |
| I0404 15:19:51.277895 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.21875 | |
| I0404 15:19:51.277907 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 15:19:51.277920 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 15:19:51.277931 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 15:19:51.277943 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 15:19:51.277956 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.59375 | |
| I0404 15:19:51.277967 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:19:51.277979 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:19:51.277990 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:19:51.278002 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:19:51.278014 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:19:51.278025 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:19:51.278038 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:19:51.278049 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:19:51.278060 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:19:51.278072 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:19:51.278084 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:19:51.278095 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:19:51.278106 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:19:51.278118 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:19:51.278129 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:19:51.278144 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.10835 (* 0.0454545 = 0.0958343 loss) | |
| I0404 15:19:51.278159 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.61062 (* 0.0454545 = 0.118665 loss) | |
| I0404 15:19:51.278172 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.93111 (* 0.0454545 = 0.133232 loss) | |
| I0404 15:19:51.278187 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.86831 (* 0.0454545 = 0.130378 loss) | |
| I0404 15:19:51.278200 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.5389 (* 0.0454545 = 0.115405 loss) | |
| I0404 15:19:51.278214 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.61938 (* 0.0454545 = 0.0736081 loss) | |
| I0404 15:19:51.278228 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.28461 (* 0.0454545 = 0.0583913 loss) | |
| I0404 15:19:51.278241 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.470054 (* 0.0454545 = 0.0213661 loss) | |
| I0404 15:19:51.278255 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.232545 (* 0.0454545 = 0.0105702 loss) | |
| I0404 15:19:51.278270 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.140355 (* 0.0454545 = 0.00637979 loss) | |
| I0404 15:19:51.278283 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.9833e-05 (* 0.0454545 = 9.015e-07 loss) | |
| I0404 15:19:51.278298 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.63885e-05 (* 0.0454545 = 7.44931e-07 loss) | |
| I0404 15:19:51.278312 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.16607e-05 (* 0.0454545 = 9.84576e-07 loss) | |
| I0404 15:19:51.278326 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.95631e-05 (* 0.0454545 = 8.89232e-07 loss) | |
| I0404 15:19:51.278339 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.61686e-05 (* 0.0454545 = 7.34938e-07 loss) | |
| I0404 15:19:51.278353 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.84341e-05 (* 0.0454545 = 8.37911e-07 loss) | |
| I0404 15:19:51.278367 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.68823e-05 (* 0.0454545 = 7.67377e-07 loss) | |
| I0404 15:19:51.278396 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.01794e-05 (* 0.0454545 = 9.17243e-07 loss) | |
| I0404 15:19:51.278412 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.59918e-05 (* 0.0454545 = 7.26899e-07 loss) | |
| I0404 15:19:51.278425 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.73517e-05 (* 0.0454545 = 7.88712e-07 loss) | |
| I0404 15:19:51.278439 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.8775e-05 (* 0.0454545 = 8.53411e-07 loss) | |
| I0404 15:19:51.278456 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.68599e-05 (* 0.0454545 = 7.66359e-07 loss) | |
| I0404 15:19:51.278478 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:19:51.278491 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000508113 | |
| I0404 15:19:51.278506 9252 sgd_solver.cpp:106] Iteration 63500, lr = 0.009365 | |
| I0404 15:21:02.493968 9252 solver.cpp:229] Iteration 64000, loss = 0.799735 | |
| I0404 15:21:02.494169 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.375 | |
| I0404 15:21:02.494197 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.21875 | |
| I0404 15:21:02.494210 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 15:21:02.494222 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.3125 | |
| I0404 15:21:02.494235 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 15:21:02.494246 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 15:21:02.494258 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 15:21:02.494271 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 15:21:02.494282 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:21:02.494294 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:21:02.494307 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:21:02.494318 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:21:02.494329 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:21:02.494341 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:21:02.494354 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:21:02.494364 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:21:02.494376 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:21:02.494387 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:21:02.494400 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:21:02.494410 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:21:02.494424 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:21:02.494436 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:21:02.494451 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.19433 (* 0.0454545 = 0.0997423 loss) | |
| I0404 15:21:02.494467 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.45606 (* 0.0454545 = 0.111639 loss) | |
| I0404 15:21:02.494489 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.74533 (* 0.0454545 = 0.124788 loss) | |
| I0404 15:21:02.494503 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.3836 (* 0.0454545 = 0.108345 loss) | |
| I0404 15:21:02.494516 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.61944 (* 0.0454545 = 0.119065 loss) | |
| I0404 15:21:02.494530 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.5501 (* 0.0454545 = 0.115914 loss) | |
| I0404 15:21:02.494544 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.28025 (* 0.0454545 = 0.0581932 loss) | |
| I0404 15:21:02.494557 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.54752 (* 0.0454545 = 0.0248873 loss) | |
| I0404 15:21:02.494571 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.232319 (* 0.0454545 = 0.01056 loss) | |
| I0404 15:21:02.494585 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0856975 (* 0.0454545 = 0.00389534 loss) | |
| I0404 15:21:02.494601 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.42303e-05 (* 0.0454545 = 1.55592e-06 loss) | |
| I0404 15:21:02.494614 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.19204e-05 (* 0.0454545 = 1.45093e-06 loss) | |
| I0404 15:21:02.494642 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.44618e-05 (* 0.0454545 = 1.56645e-06 loss) | |
| I0404 15:21:02.494657 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.36271e-05 (* 0.0454545 = 1.5285e-06 loss) | |
| I0404 15:21:02.494670 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.09666e-05 (* 0.0454545 = 1.40757e-06 loss) | |
| I0404 15:21:02.494684 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.55719e-05 (* 0.0454545 = 1.6169e-06 loss) | |
| I0404 15:21:02.494699 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.17565e-05 (* 0.0454545 = 1.44348e-06 loss) | |
| I0404 15:21:02.494726 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.14194e-05 (* 0.0454545 = 1.42815e-06 loss) | |
| I0404 15:21:02.494741 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.0188e-05 (* 0.0454545 = 1.37218e-06 loss) | |
| I0404 15:21:02.494758 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.99477e-05 (* 0.0454545 = 1.36126e-06 loss) | |
| I0404 15:21:02.494772 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.97353e-05 (* 0.0454545 = 1.35161e-06 loss) | |
| I0404 15:21:02.494786 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.05237e-05 (* 0.0454545 = 1.38744e-06 loss) | |
| I0404 15:21:02.494798 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 15:21:02.494810 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00156889 | |
| I0404 15:21:02.494823 9252 sgd_solver.cpp:106] Iteration 64000, lr = 0.00936 | |
| I0404 15:22:14.333143 9252 solver.cpp:229] Iteration 64500, loss = 0.796128 | |
| I0404 15:22:14.333259 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.3125 | |
| I0404 15:22:14.333289 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 15:22:14.333314 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 15:22:14.333338 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 15:22:14.333359 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 15:22:14.333382 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 15:22:14.333405 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 15:22:14.333444 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 15:22:14.333478 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:22:14.333500 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:22:14.333533 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:22:14.333556 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:22:14.333586 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:22:14.333607 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:22:14.333628 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:22:14.333650 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:22:14.333672 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:22:14.333693 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:22:14.333714 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:22:14.333734 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:22:14.333756 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:22:14.333781 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:22:14.333808 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.49675 (* 0.0454545 = 0.113489 loss) | |
| I0404 15:22:14.333835 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.24955 (* 0.0454545 = 0.147707 loss) | |
| I0404 15:22:14.333861 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.09588 (* 0.0454545 = 0.140722 loss) | |
| I0404 15:22:14.333886 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.0821 (* 0.0454545 = 0.140095 loss) | |
| I0404 15:22:14.333914 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.7921 (* 0.0454545 = 0.126913 loss) | |
| I0404 15:22:14.333945 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.2206 (* 0.0454545 = 0.100936 loss) | |
| I0404 15:22:14.333976 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.99365 (* 0.0454545 = 0.0451659 loss) | |
| I0404 15:22:14.334018 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.259945 (* 0.0454545 = 0.0118157 loss) | |
| I0404 15:22:14.334045 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.146469 (* 0.0454545 = 0.00665766 loss) | |
| I0404 15:22:14.334080 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.169051 (* 0.0454545 = 0.00768414 loss) | |
| I0404 15:22:14.334106 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.25855e-06 (* 0.0454545 = 2.84479e-07 loss) | |
| I0404 15:22:14.334132 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.32349e-06 (* 0.0454545 = 2.41977e-07 loss) | |
| I0404 15:22:14.334158 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.7519e-06 (* 0.0454545 = 2.6145e-07 loss) | |
| I0404 15:22:14.334185 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.5023e-06 (* 0.0454545 = 2.50105e-07 loss) | |
| I0404 15:22:14.334211 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.14094e-06 (* 0.0454545 = 2.33679e-07 loss) | |
| I0404 15:22:14.334238 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.43364e-06 (* 0.0454545 = 2.92438e-07 loss) | |
| I0404 15:22:14.334264 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.9435e-06 (* 0.0454545 = 2.24705e-07 loss) | |
| I0404 15:22:14.334314 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.16701e-06 (* 0.0454545 = 2.34864e-07 loss) | |
| I0404 15:22:14.334352 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.49858e-06 (* 0.0454545 = 2.49935e-07 loss) | |
| I0404 15:22:14.334379 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.53583e-06 (* 0.0454545 = 2.51629e-07 loss) | |
| I0404 15:22:14.334413 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.39587e-06 (* 0.0454545 = 1.99812e-07 loss) | |
| I0404 15:22:14.334439 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.73115e-06 (* 0.0454545 = 2.15052e-07 loss) | |
| I0404 15:22:14.334461 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:22:14.334483 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00174092 | |
| I0404 15:22:14.334506 9252 sgd_solver.cpp:106] Iteration 64500, lr = 0.009355 | |
| I0404 15:23:26.137974 9252 solver.cpp:229] Iteration 65000, loss = 0.795294 | |
| I0404 15:23:26.138101 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.46875 | |
| I0404 15:23:26.138121 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.21875 | |
| I0404 15:23:26.138134 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.28125 | |
| I0404 15:23:26.138146 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 15:23:26.138159 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.375 | |
| I0404 15:23:26.138170 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 15:23:26.138182 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 15:23:26.138195 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:23:26.138206 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:23:26.138218 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 15:23:26.138229 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:23:26.138242 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:23:26.138253 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:23:26.138264 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:23:26.138276 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:23:26.138288 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:23:26.138299 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:23:26.138310 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:23:26.138322 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:23:26.138334 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:23:26.138345 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:23:26.138356 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:23:26.138372 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.93035 (* 0.0454545 = 0.0877432 loss) | |
| I0404 15:23:26.138386 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.73065 (* 0.0454545 = 0.124121 loss) | |
| I0404 15:23:26.138401 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.562 (* 0.0454545 = 0.116455 loss) | |
| I0404 15:23:26.138414 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.84941 (* 0.0454545 = 0.129519 loss) | |
| I0404 15:23:26.138428 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.23498 (* 0.0454545 = 0.10159 loss) | |
| I0404 15:23:26.138442 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.60916 (* 0.0454545 = 0.0731436 loss) | |
| I0404 15:23:26.138455 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.924704 (* 0.0454545 = 0.042032 loss) | |
| I0404 15:23:26.138469 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.506442 (* 0.0454545 = 0.0230201 loss) | |
| I0404 15:23:26.138484 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.195364 (* 0.0454545 = 0.0088802 loss) | |
| I0404 15:23:26.138496 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.235976 (* 0.0454545 = 0.0107262 loss) | |
| I0404 15:23:26.138511 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.86348e-05 (* 0.0454545 = 2.21067e-06 loss) | |
| I0404 15:23:26.138526 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.24547e-05 (* 0.0454545 = 1.92976e-06 loss) | |
| I0404 15:23:26.138540 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.33772e-05 (* 0.0454545 = 2.42624e-06 loss) | |
| I0404 15:23:26.138553 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.42362e-05 (* 0.0454545 = 2.01074e-06 loss) | |
| I0404 15:23:26.138567 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.33175e-05 (* 0.0454545 = 1.96898e-06 loss) | |
| I0404 15:23:26.138581 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.7429e-05 (* 0.0454545 = 2.15586e-06 loss) | |
| I0404 15:23:26.138595 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.44095e-05 (* 0.0454545 = 2.01861e-06 loss) | |
| I0404 15:23:26.138627 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.77923e-05 (* 0.0454545 = 2.17238e-06 loss) | |
| I0404 15:23:26.138641 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.94188e-05 (* 0.0454545 = 1.79177e-06 loss) | |
| I0404 15:23:26.138655 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.88062e-05 (* 0.0454545 = 2.21846e-06 loss) | |
| I0404 15:23:26.138669 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.38331e-05 (* 0.0454545 = 1.99241e-06 loss) | |
| I0404 15:23:26.138684 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.69414e-05 (* 0.0454545 = 2.1337e-06 loss) | |
| I0404 15:23:26.138695 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:23:26.138707 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0025781 | |
| I0404 15:23:26.138721 9252 sgd_solver.cpp:106] Iteration 65000, lr = 0.00935 | |
| I0404 15:24:36.976475 9252 solver.cpp:229] Iteration 65500, loss = 0.789756 | |
| I0404 15:24:36.976625 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.46875 | |
| I0404 15:24:36.976646 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.1875 | |
| I0404 15:24:36.976660 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 15:24:36.976671 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.09375 | |
| I0404 15:24:36.976683 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.375 | |
| I0404 15:24:36.976696 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 15:24:36.976707 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 15:24:36.976725 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 15:24:36.976737 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:24:36.976752 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 15:24:36.976763 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:24:36.976775 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:24:36.976788 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:24:36.976799 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:24:36.976810 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:24:36.976830 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:24:36.976840 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:24:36.976852 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:24:36.976864 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:24:36.976876 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:24:36.976887 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:24:36.976907 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:24:36.976923 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.90614 (* 0.0454545 = 0.0866428 loss) | |
| I0404 15:24:36.976938 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.77746 (* 0.0454545 = 0.126248 loss) | |
| I0404 15:24:36.976951 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.86382 (* 0.0454545 = 0.130174 loss) | |
| I0404 15:24:36.976964 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.13133 (* 0.0454545 = 0.142333 loss) | |
| I0404 15:24:36.976979 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.34981 (* 0.0454545 = 0.10681 loss) | |
| I0404 15:24:36.976992 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.01182 (* 0.0454545 = 0.0914466 loss) | |
| I0404 15:24:36.977005 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.40728 (* 0.0454545 = 0.0639671 loss) | |
| I0404 15:24:36.977020 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.713331 (* 0.0454545 = 0.0324241 loss) | |
| I0404 15:24:36.977032 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.310898 (* 0.0454545 = 0.0141317 loss) | |
| I0404 15:24:36.977046 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.368411 (* 0.0454545 = 0.016746 loss) | |
| I0404 15:24:36.977069 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.00778e-05 (* 0.0454545 = 9.12626e-07 loss) | |
| I0404 15:24:36.977083 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.64056e-05 (* 0.0454545 = 7.4571e-07 loss) | |
| I0404 15:24:36.977097 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.11308e-05 (* 0.0454545 = 9.60493e-07 loss) | |
| I0404 15:24:36.977110 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.88781e-05 (* 0.0454545 = 8.58094e-07 loss) | |
| I0404 15:24:36.977131 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.79315e-05 (* 0.0454545 = 8.15069e-07 loss) | |
| I0404 15:24:36.977145 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.93661e-05 (* 0.0454545 = 8.80278e-07 loss) | |
| I0404 15:24:36.977159 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.90979e-05 (* 0.0454545 = 8.68086e-07 loss) | |
| I0404 15:24:36.977186 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.87288e-05 (* 0.0454545 = 8.51308e-07 loss) | |
| I0404 15:24:36.977201 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.90643e-05 (* 0.0454545 = 8.6656e-07 loss) | |
| I0404 15:24:36.977218 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.84197e-05 (* 0.0454545 = 8.3726e-07 loss) | |
| I0404 15:24:36.977249 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.89862e-05 (* 0.0454545 = 8.63009e-07 loss) | |
| I0404 15:24:36.977264 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.84683e-05 (* 0.0454545 = 8.39468e-07 loss) | |
| I0404 15:24:36.977277 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:24:36.977288 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0017042 | |
| I0404 15:24:36.977301 9252 sgd_solver.cpp:106] Iteration 65500, lr = 0.009345 | |
| I0404 15:25:48.205828 9252 solver.cpp:229] Iteration 66000, loss = 0.785441 | |
| I0404 15:25:48.206065 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.34375 | |
| I0404 15:25:48.206087 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.0625 | |
| I0404 15:25:48.206100 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 15:25:48.206112 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 15:25:48.206125 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.1875 | |
| I0404 15:25:48.206136 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 15:25:48.206148 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:25:48.206161 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:25:48.206172 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.875 | |
| I0404 15:25:48.206184 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 15:25:48.206195 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:25:48.206207 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:25:48.206219 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:25:48.206230 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:25:48.206243 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:25:48.206254 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:25:48.206265 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:25:48.206277 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:25:48.206289 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:25:48.206300 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:25:48.206311 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:25:48.206322 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:25:48.206338 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.22234 (* 0.0454545 = 0.101015 loss) | |
| I0404 15:25:48.206352 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.69318 (* 0.0454545 = 0.122417 loss) | |
| I0404 15:25:48.206367 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.23899 (* 0.0454545 = 0.147227 loss) | |
| I0404 15:25:48.206379 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.02787 (* 0.0454545 = 0.13763 loss) | |
| I0404 15:25:48.206393 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.10018 (* 0.0454545 = 0.140917 loss) | |
| I0404 15:25:48.206408 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.26733 (* 0.0454545 = 0.103061 loss) | |
| I0404 15:25:48.206420 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.35812 (* 0.0454545 = 0.0617327 loss) | |
| I0404 15:25:48.206434 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.649996 (* 0.0454545 = 0.0295453 loss) | |
| I0404 15:25:48.206447 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.603444 (* 0.0454545 = 0.0274293 loss) | |
| I0404 15:25:48.206461 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.441534 (* 0.0454545 = 0.0200697 loss) | |
| I0404 15:25:48.206475 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.71596e-05 (* 0.0454545 = 1.23453e-06 loss) | |
| I0404 15:25:48.206490 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.24817e-05 (* 0.0454545 = 1.0219e-06 loss) | |
| I0404 15:25:48.206503 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.07684e-05 (* 0.0454545 = 1.39856e-06 loss) | |
| I0404 15:25:48.206517 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.59695e-05 (* 0.0454545 = 1.18043e-06 loss) | |
| I0404 15:25:48.206532 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.41283e-05 (* 0.0454545 = 1.09674e-06 loss) | |
| I0404 15:25:48.206545 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.34133e-05 (* 0.0454545 = 1.06424e-06 loss) | |
| I0404 15:25:48.206558 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.35436e-05 (* 0.0454545 = 1.07016e-06 loss) | |
| I0404 15:25:48.206590 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.48737e-05 (* 0.0454545 = 1.13062e-06 loss) | |
| I0404 15:25:48.206606 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.15505e-05 (* 0.0454545 = 9.79567e-07 loss) | |
| I0404 15:25:48.206619 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.32529e-05 (* 0.0454545 = 1.05695e-06 loss) | |
| I0404 15:25:48.206634 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.3078e-05 (* 0.0454545 = 1.049e-06 loss) | |
| I0404 15:25:48.206647 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.31189e-05 (* 0.0454545 = 1.05086e-06 loss) | |
| I0404 15:25:48.206660 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:25:48.206671 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00187737 | |
| I0404 15:25:48.206686 9252 sgd_solver.cpp:106] Iteration 66000, lr = 0.00934 | |
| I0404 15:26:59.584898 9252 solver.cpp:229] Iteration 66500, loss = 0.784113 | |
| I0404 15:26:59.585047 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.375 | |
| I0404 15:26:59.585068 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.3125 | |
| I0404 15:26:59.585080 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.25 | |
| I0404 15:26:59.585093 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 15:26:59.585104 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 15:26:59.585116 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 15:26:59.585129 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 15:26:59.585140 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 15:26:59.585152 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:26:59.585165 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:26:59.585175 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:26:59.585187 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:26:59.585199 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:26:59.585211 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:26:59.585222 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:26:59.585233 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:26:59.585244 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:26:59.585256 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:26:59.585268 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:26:59.585279 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:26:59.585290 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:26:59.585301 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:26:59.585317 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.96978 (* 0.0454545 = 0.0895356 loss) | |
| I0404 15:26:59.585332 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.50389 (* 0.0454545 = 0.113813 loss) | |
| I0404 15:26:59.585346 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.64874 (* 0.0454545 = 0.120397 loss) | |
| I0404 15:26:59.585360 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.95055 (* 0.0454545 = 0.134116 loss) | |
| I0404 15:26:59.585373 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.19077 (* 0.0454545 = 0.0995804 loss) | |
| I0404 15:26:59.585387 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.01651 (* 0.0454545 = 0.0916597 loss) | |
| I0404 15:26:59.585400 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.56548 (* 0.0454545 = 0.071158 loss) | |
| I0404 15:26:59.585414 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.252893 (* 0.0454545 = 0.0114951 loss) | |
| I0404 15:26:59.585445 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.122131 (* 0.0454545 = 0.00555139 loss) | |
| I0404 15:26:59.585460 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.137232 (* 0.0454545 = 0.00623782 loss) | |
| I0404 15:26:59.585474 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.44141e-05 (* 0.0454545 = 6.55186e-07 loss) | |
| I0404 15:26:59.585489 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.12547e-05 (* 0.0454545 = 5.11577e-07 loss) | |
| I0404 15:26:59.585502 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.64372e-05 (* 0.0454545 = 7.47146e-07 loss) | |
| I0404 15:26:59.585517 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.49581e-05 (* 0.0454545 = 6.79914e-07 loss) | |
| I0404 15:26:59.585531 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.13068e-05 (* 0.0454545 = 5.13944e-07 loss) | |
| I0404 15:26:59.585544 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.46562e-05 (* 0.0454545 = 6.6619e-07 loss) | |
| I0404 15:26:59.585559 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.34416e-05 (* 0.0454545 = 6.10982e-07 loss) | |
| I0404 15:26:59.585592 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.36205e-05 (* 0.0454545 = 6.19112e-07 loss) | |
| I0404 15:26:59.585608 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.3002e-05 (* 0.0454545 = 5.91002e-07 loss) | |
| I0404 15:26:59.585621 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.36354e-05 (* 0.0454545 = 6.1979e-07 loss) | |
| I0404 15:26:59.585635 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.30691e-05 (* 0.0454545 = 5.9405e-07 loss) | |
| I0404 15:26:59.585649 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.32628e-05 (* 0.0454545 = 6.02856e-07 loss) | |
| I0404 15:26:59.585661 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 15:26:59.585674 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0024326 | |
| I0404 15:26:59.585687 9252 sgd_solver.cpp:106] Iteration 66500, lr = 0.009335 | |
| I0404 15:28:10.633896 9252 solver.cpp:229] Iteration 67000, loss = 0.782037 | |
| I0404 15:28:10.633998 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.4375 | |
| I0404 15:28:10.634017 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.15625 | |
| I0404 15:28:10.634029 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 15:28:10.634042 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 15:28:10.634057 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 15:28:10.634070 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 15:28:10.634083 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.59375 | |
| I0404 15:28:10.634094 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 15:28:10.634106 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 15:28:10.634119 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:28:10.634130 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:28:10.634141 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:28:10.634153 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:28:10.634165 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:28:10.634176 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:28:10.634187 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:28:10.634199 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:28:10.634210 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:28:10.634222 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:28:10.634233 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:28:10.634244 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:28:10.634255 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:28:10.634270 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.07343 (* 0.0454545 = 0.0942466 loss) | |
| I0404 15:28:10.634285 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.97313 (* 0.0454545 = 0.135142 loss) | |
| I0404 15:28:10.634299 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.12314 (* 0.0454545 = 0.141961 loss) | |
| I0404 15:28:10.634312 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.00822 (* 0.0454545 = 0.136737 loss) | |
| I0404 15:28:10.634326 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.65212 (* 0.0454545 = 0.120551 loss) | |
| I0404 15:28:10.634340 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.34716 (* 0.0454545 = 0.106689 loss) | |
| I0404 15:28:10.634353 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.67666 (* 0.0454545 = 0.0762116 loss) | |
| I0404 15:28:10.634366 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.83047 (* 0.0454545 = 0.0377486 loss) | |
| I0404 15:28:10.634382 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0762055 (* 0.0454545 = 0.00346389 loss) | |
| I0404 15:28:10.634395 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.027178 (* 0.0454545 = 0.00123536 loss) | |
| I0404 15:28:10.634409 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.87866e-05 (* 0.0454545 = 1.76303e-06 loss) | |
| I0404 15:28:10.634423 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.98413e-05 (* 0.0454545 = 1.35642e-06 loss) | |
| I0404 15:28:10.634438 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.33667e-05 (* 0.0454545 = 1.51667e-06 loss) | |
| I0404 15:28:10.634452 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.21187e-05 (* 0.0454545 = 1.45994e-06 loss) | |
| I0404 15:28:10.634466 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.21943e-05 (* 0.0454545 = 1.46338e-06 loss) | |
| I0404 15:28:10.634480 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.54334e-05 (* 0.0454545 = 1.61061e-06 loss) | |
| I0404 15:28:10.634495 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.1873e-05 (* 0.0454545 = 1.44877e-06 loss) | |
| I0404 15:28:10.634524 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.92684e-05 (* 0.0454545 = 1.33038e-06 loss) | |
| I0404 15:28:10.634539 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.05002e-05 (* 0.0454545 = 1.38637e-06 loss) | |
| I0404 15:28:10.634553 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.225e-05 (* 0.0454545 = 1.46591e-06 loss) | |
| I0404 15:28:10.634567 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.09849e-05 (* 0.0454545 = 1.4084e-06 loss) | |
| I0404 15:28:10.634582 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.01166e-05 (* 0.0454545 = 1.36894e-06 loss) | |
| I0404 15:28:10.634593 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:28:10.634605 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00236827 | |
| I0404 15:28:10.634618 9252 sgd_solver.cpp:106] Iteration 67000, lr = 0.00933 | |
| I0404 15:29:21.514879 9252 solver.cpp:229] Iteration 67500, loss = 0.782111 | |
| I0404 15:29:21.515027 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.5 | |
| I0404 15:29:21.515048 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.25 | |
| I0404 15:29:21.515060 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.1875 | |
| I0404 15:29:21.515072 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 15:29:21.515085 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 15:29:21.515096 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 15:29:21.515108 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:29:21.515120 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 15:29:21.515133 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 15:29:21.515146 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:29:21.515156 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:29:21.515168 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:29:21.515179 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:29:21.515192 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:29:21.515202 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:29:21.515214 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:29:21.515225 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:29:21.515236 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:29:21.515249 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:29:21.515259 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:29:21.515271 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:29:21.515282 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:29:21.515298 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.19563 (* 0.0454545 = 0.0998015 loss) | |
| I0404 15:29:21.515312 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.7171 (* 0.0454545 = 0.123504 loss) | |
| I0404 15:29:21.515326 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.94328 (* 0.0454545 = 0.133785 loss) | |
| I0404 15:29:21.515339 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.91564 (* 0.0454545 = 0.132529 loss) | |
| I0404 15:29:21.515353 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.49742 (* 0.0454545 = 0.113519 loss) | |
| I0404 15:29:21.515367 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.73423 (* 0.0454545 = 0.124283 loss) | |
| I0404 15:29:21.515380 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.96466 (* 0.0454545 = 0.0438482 loss) | |
| I0404 15:29:21.515394 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.145761 (* 0.0454545 = 0.00662548 loss) | |
| I0404 15:29:21.515408 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0199018 (* 0.0454545 = 0.000904627 loss) | |
| I0404 15:29:21.515422 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00705722 (* 0.0454545 = 0.000320783 loss) | |
| I0404 15:29:21.515437 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.48757e-05 (* 0.0454545 = 6.76169e-07 loss) | |
| I0404 15:29:21.515450 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.59135e-05 (* 0.0454545 = 7.23341e-07 loss) | |
| I0404 15:29:21.515465 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.68391e-05 (* 0.0454545 = 7.65412e-07 loss) | |
| I0404 15:29:21.515478 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.38922e-05 (* 0.0454545 = 6.31464e-07 loss) | |
| I0404 15:29:21.515492 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.9721e-05 (* 0.0454545 = 8.96411e-07 loss) | |
| I0404 15:29:21.515506 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.48682e-05 (* 0.0454545 = 6.75825e-07 loss) | |
| I0404 15:29:21.515521 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.34413e-05 (* 0.0454545 = 6.10969e-07 loss) | |
| I0404 15:29:21.515547 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.65522e-05 (* 0.0454545 = 7.52373e-07 loss) | |
| I0404 15:29:21.515566 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.51124e-05 (* 0.0454545 = 6.86926e-07 loss) | |
| I0404 15:29:21.515581 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.64814e-05 (* 0.0454545 = 7.49154e-07 loss) | |
| I0404 15:29:21.515595 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.48607e-05 (* 0.0454545 = 6.75488e-07 loss) | |
| I0404 15:29:21.515609 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.64479e-05 (* 0.0454545 = 7.47632e-07 loss) | |
| I0404 15:29:21.515620 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:29:21.515631 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00709756 | |
| I0404 15:29:21.515645 9252 sgd_solver.cpp:106] Iteration 67500, lr = 0.009325 | |
| I0404 15:30:32.393697 9252 solver.cpp:229] Iteration 68000, loss = 0.775137 | |
| I0404 15:30:32.393815 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.40625 | |
| I0404 15:30:32.393833 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 15:30:32.393846 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 15:30:32.393859 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 15:30:32.393872 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.15625 | |
| I0404 15:30:32.393882 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.28125 | |
| I0404 15:30:32.393894 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:30:32.393909 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:30:32.393921 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:30:32.393934 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:30:32.393945 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:30:32.393957 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:30:32.393968 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:30:32.393980 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:30:32.393991 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:30:32.394003 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:30:32.394016 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:30:32.394026 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:30:32.394038 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:30:32.394049 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:30:32.394062 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:30:32.394073 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:30:32.394088 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.92618 (* 0.0454545 = 0.0875538 loss) | |
| I0404 15:30:32.394103 9252 solver.cpp:245] Train net output #23: loss/loss02 = 3.21383 (* 0.0454545 = 0.146083 loss) | |
| I0404 15:30:32.394116 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.24764 (* 0.0454545 = 0.14762 loss) | |
| I0404 15:30:32.394129 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.07713 (* 0.0454545 = 0.13987 loss) | |
| I0404 15:30:32.394143 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.02391 (* 0.0454545 = 0.13745 loss) | |
| I0404 15:30:32.394157 9252 solver.cpp:245] Train net output #27: loss/loss06 = 3.01906 (* 0.0454545 = 0.13723 loss) | |
| I0404 15:30:32.394170 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.04562 (* 0.0454545 = 0.0475281 loss) | |
| I0404 15:30:32.394183 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.700436 (* 0.0454545 = 0.031838 loss) | |
| I0404 15:30:32.394201 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.311298 (* 0.0454545 = 0.0141499 loss) | |
| I0404 15:30:32.394217 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0217926 (* 0.0454545 = 0.000990573 loss) | |
| I0404 15:30:32.394230 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.40955e-05 (* 0.0454545 = 6.40705e-07 loss) | |
| I0404 15:30:32.394244 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.46531e-05 (* 0.0454545 = 6.66048e-07 loss) | |
| I0404 15:30:32.394258 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.23072e-05 (* 0.0454545 = 5.59419e-07 loss) | |
| I0404 15:30:32.394273 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.46324e-05 (* 0.0454545 = 6.65108e-07 loss) | |
| I0404 15:30:32.394286 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.28029e-05 (* 0.0454545 = 5.8195e-07 loss) | |
| I0404 15:30:32.394300 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.56532e-05 (* 0.0454545 = 7.1151e-07 loss) | |
| I0404 15:30:32.394315 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.22067e-05 (* 0.0454545 = 5.54852e-07 loss) | |
| I0404 15:30:32.394345 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.38049e-05 (* 0.0454545 = 6.27497e-07 loss) | |
| I0404 15:30:32.394359 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.14913e-05 (* 0.0454545 = 5.22332e-07 loss) | |
| I0404 15:30:32.394373 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.27619e-05 (* 0.0454545 = 5.80085e-07 loss) | |
| I0404 15:30:32.394387 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.40511e-05 (* 0.0454545 = 6.38685e-07 loss) | |
| I0404 15:30:32.394402 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.29035e-05 (* 0.0454545 = 5.86524e-07 loss) | |
| I0404 15:30:32.394413 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:30:32.394424 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0007861 | |
| I0404 15:30:32.394438 9252 sgd_solver.cpp:106] Iteration 68000, lr = 0.00932 | |
| I0404 15:31:43.198230 9252 solver.cpp:229] Iteration 68500, loss = 0.772723 | |
| I0404 15:31:43.198339 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.375 | |
| I0404 15:31:43.198359 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.28125 | |
| I0404 15:31:43.198372 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 15:31:43.198385 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.25 | |
| I0404 15:31:43.198396 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 15:31:43.198407 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 15:31:43.198420 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 15:31:43.198432 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:31:43.198443 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:31:43.198456 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:31:43.198467 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:31:43.198478 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:31:43.198489 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:31:43.198501 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:31:43.198513 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:31:43.198524 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:31:43.198535 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:31:43.198546 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:31:43.198559 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:31:43.198570 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:31:43.198580 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:31:43.198592 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:31:43.198607 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.02493 (* 0.0454545 = 0.0920421 loss) | |
| I0404 15:31:43.198621 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.23268 (* 0.0454545 = 0.101486 loss) | |
| I0404 15:31:43.198635 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.59889 (* 0.0454545 = 0.118131 loss) | |
| I0404 15:31:43.198649 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.78162 (* 0.0454545 = 0.126437 loss) | |
| I0404 15:31:43.198662 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.55807 (* 0.0454545 = 0.116276 loss) | |
| I0404 15:31:43.198676 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.14202 (* 0.0454545 = 0.0973647 loss) | |
| I0404 15:31:43.198689 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.879898 (* 0.0454545 = 0.0399954 loss) | |
| I0404 15:31:43.198704 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.512954 (* 0.0454545 = 0.0233161 loss) | |
| I0404 15:31:43.198716 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.312594 (* 0.0454545 = 0.0142088 loss) | |
| I0404 15:31:43.198730 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0118163 (* 0.0454545 = 0.000537105 loss) | |
| I0404 15:31:43.198748 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.54331e-05 (* 0.0454545 = 2.06514e-06 loss) | |
| I0404 15:31:43.198762 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.35335e-05 (* 0.0454545 = 1.9788e-06 loss) | |
| I0404 15:31:43.198776 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.08555e-05 (* 0.0454545 = 2.31161e-06 loss) | |
| I0404 15:31:43.198791 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.59451e-05 (* 0.0454545 = 1.63387e-06 loss) | |
| I0404 15:31:43.198804 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.73486e-05 (* 0.0454545 = 1.69766e-06 loss) | |
| I0404 15:31:43.198818 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.16434e-05 (* 0.0454545 = 1.43834e-06 loss) | |
| I0404 15:31:43.198832 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.18365e-05 (* 0.0454545 = 1.44711e-06 loss) | |
| I0404 15:31:43.198863 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.31614e-05 (* 0.0454545 = 1.50734e-06 loss) | |
| I0404 15:31:43.198879 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.09508e-05 (* 0.0454545 = 1.40685e-06 loss) | |
| I0404 15:31:43.198892 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.33032e-05 (* 0.0454545 = 1.51378e-06 loss) | |
| I0404 15:31:43.198906 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.91947e-05 (* 0.0454545 = 1.32703e-06 loss) | |
| I0404 15:31:43.198920 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.07719e-05 (* 0.0454545 = 1.39872e-06 loss) | |
| I0404 15:31:43.198931 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:31:43.198943 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00133227 | |
| I0404 15:31:43.198956 9252 sgd_solver.cpp:106] Iteration 68500, lr = 0.009315 | |
| I0404 15:32:54.699092 9252 solver.cpp:229] Iteration 69000, loss = 0.773808 | |
| I0404 15:32:54.699239 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.4375 | |
| I0404 15:32:54.699262 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.25 | |
| I0404 15:32:54.699276 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.1875 | |
| I0404 15:32:54.699288 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.34375 | |
| I0404 15:32:54.699301 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 15:32:54.699313 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 15:32:54.699326 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 15:32:54.699337 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 15:32:54.699349 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:32:54.699362 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:32:54.699373 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:32:54.699384 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:32:54.699396 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:32:54.699409 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:32:54.699419 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:32:54.699431 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:32:54.699442 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:32:54.699455 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:32:54.699466 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:32:54.699478 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:32:54.699491 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:32:54.699501 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:32:54.699517 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.317 (* 0.0454545 = 0.105318 loss) | |
| I0404 15:32:54.699540 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.44591 (* 0.0454545 = 0.111178 loss) | |
| I0404 15:32:54.699555 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.92565 (* 0.0454545 = 0.132984 loss) | |
| I0404 15:32:54.699570 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.34907 (* 0.0454545 = 0.106776 loss) | |
| I0404 15:32:54.699594 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.53781 (* 0.0454545 = 0.115355 loss) | |
| I0404 15:32:54.699610 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.82912 (* 0.0454545 = 0.0831418 loss) | |
| I0404 15:32:54.699625 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.784908 (* 0.0454545 = 0.0356777 loss) | |
| I0404 15:32:54.699638 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.606932 (* 0.0454545 = 0.0275878 loss) | |
| I0404 15:32:54.699652 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.314491 (* 0.0454545 = 0.0142951 loss) | |
| I0404 15:32:54.699666 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.178167 (* 0.0454545 = 0.0080985 loss) | |
| I0404 15:32:54.699681 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.25758e-05 (* 0.0454545 = 1.48072e-06 loss) | |
| I0404 15:32:54.699695 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.68877e-05 (* 0.0454545 = 1.22217e-06 loss) | |
| I0404 15:32:54.699709 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.85369e-05 (* 0.0454545 = 1.29713e-06 loss) | |
| I0404 15:32:54.699723 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.33995e-05 (* 0.0454545 = 1.51816e-06 loss) | |
| I0404 15:32:54.699738 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.03381e-05 (* 0.0454545 = 1.379e-06 loss) | |
| I0404 15:32:54.699754 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.84109e-05 (* 0.0454545 = 1.74595e-06 loss) | |
| I0404 15:32:54.699769 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.83056e-05 (* 0.0454545 = 1.28662e-06 loss) | |
| I0404 15:32:54.699805 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.86598e-05 (* 0.0454545 = 1.30272e-06 loss) | |
| I0404 15:32:54.699820 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.19349e-05 (* 0.0454545 = 1.45159e-06 loss) | |
| I0404 15:32:54.699834 9252 solver.cpp:245] Train net output #41: loss/loss20 = 3.15773e-05 (* 0.0454545 = 1.43533e-06 loss) | |
| I0404 15:32:54.699848 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.02119e-05 (* 0.0454545 = 1.37327e-06 loss) | |
| I0404 15:32:54.699862 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.70034e-05 (* 0.0454545 = 1.22743e-06 loss) | |
| I0404 15:32:54.699880 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:32:54.699893 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00354865 | |
| I0404 15:32:54.699914 9252 sgd_solver.cpp:106] Iteration 69000, lr = 0.00931 | |
| I0404 15:34:05.863241 9252 solver.cpp:229] Iteration 69500, loss = 0.770727 | |
| I0404 15:34:05.863402 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.5625 | |
| I0404 15:34:05.863427 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.28125 | |
| I0404 15:34:05.863440 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.09375 | |
| I0404 15:34:05.863452 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 15:34:05.863464 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 15:34:05.863482 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 15:34:05.863494 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:34:05.863507 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 15:34:05.863518 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:34:05.863530 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:34:05.863541 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:34:05.863553 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:34:05.863564 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:34:05.863575 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:34:05.863586 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:34:05.863598 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:34:05.863610 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:34:05.863621 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:34:05.863633 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:34:05.863647 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:34:05.863659 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:34:05.863670 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:34:05.863692 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.9156 (* 0.0454545 = 0.0870729 loss) | |
| I0404 15:34:05.863720 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.79423 (* 0.0454545 = 0.12701 loss) | |
| I0404 15:34:05.863734 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.12678 (* 0.0454545 = 0.142126 loss) | |
| I0404 15:34:05.863750 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.79705 (* 0.0454545 = 0.127139 loss) | |
| I0404 15:34:05.863765 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.59182 (* 0.0454545 = 0.11781 loss) | |
| I0404 15:34:05.863778 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.8619 (* 0.0454545 = 0.0846316 loss) | |
| I0404 15:34:05.863792 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.36504 (* 0.0454545 = 0.0620474 loss) | |
| I0404 15:34:05.863806 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.209799 (* 0.0454545 = 0.00953633 loss) | |
| I0404 15:34:05.863821 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.101164 (* 0.0454545 = 0.00459839 loss) | |
| I0404 15:34:05.863838 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0365075 (* 0.0454545 = 0.00165943 loss) | |
| I0404 15:34:05.863862 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.49038e-05 (* 0.0454545 = 6.77446e-07 loss) | |
| I0404 15:34:05.863878 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.20258e-05 (* 0.0454545 = 5.46626e-07 loss) | |
| I0404 15:34:05.863893 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.62379e-05 (* 0.0454545 = 7.38086e-07 loss) | |
| I0404 15:34:05.863914 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.36055e-05 (* 0.0454545 = 6.1843e-07 loss) | |
| I0404 15:34:05.863955 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.36948e-05 (* 0.0454545 = 6.22491e-07 loss) | |
| I0404 15:34:05.863970 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.50007e-05 (* 0.0454545 = 6.81851e-07 loss) | |
| I0404 15:34:05.863982 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.19774e-05 (* 0.0454545 = 5.44425e-07 loss) | |
| I0404 15:34:05.864018 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.20891e-05 (* 0.0454545 = 5.49502e-07 loss) | |
| I0404 15:34:05.864033 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.2484e-05 (* 0.0454545 = 5.67457e-07 loss) | |
| I0404 15:34:05.864048 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.17799e-05 (* 0.0454545 = 5.3545e-07 loss) | |
| I0404 15:34:05.864061 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.25623e-05 (* 0.0454545 = 5.71014e-07 loss) | |
| I0404 15:34:05.864076 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.14931e-05 (* 0.0454545 = 5.22412e-07 loss) | |
| I0404 15:34:05.864089 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:34:05.864099 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000808003 | |
| I0404 15:34:05.864114 9252 sgd_solver.cpp:106] Iteration 69500, lr = 0.009305 | |
| I0404 15:35:16.371008 9252 solver.cpp:338] Iteration 70000, Testing net (#0) | |
| I0404 15:35:24.356556 9252 solver.cpp:393] Test loss: 0.639516 | |
| I0404 15:35:24.356606 9252 solver.cpp:406] Test net output #0: loss/accuracy01 = 0.453 | |
| I0404 15:35:24.356621 9252 solver.cpp:406] Test net output #1: loss/accuracy02 = 0.242 | |
| I0404 15:35:24.356633 9252 solver.cpp:406] Test net output #2: loss/accuracy03 = 0.194 | |
| I0404 15:35:24.356645 9252 solver.cpp:406] Test net output #3: loss/accuracy04 = 0.218 | |
| I0404 15:35:24.356657 9252 solver.cpp:406] Test net output #4: loss/accuracy05 = 0.316 | |
| I0404 15:35:24.356668 9252 solver.cpp:406] Test net output #5: loss/accuracy06 = 0.57 | |
| I0404 15:35:24.356680 9252 solver.cpp:406] Test net output #6: loss/accuracy07 = 0.901 | |
| I0404 15:35:24.356691 9252 solver.cpp:406] Test net output #7: loss/accuracy08 = 0.971 | |
| I0404 15:35:24.356703 9252 solver.cpp:406] Test net output #8: loss/accuracy09 = 0.995 | |
| I0404 15:35:24.356714 9252 solver.cpp:406] Test net output #9: loss/accuracy10 = 0.998 | |
| I0404 15:35:24.356725 9252 solver.cpp:406] Test net output #10: loss/accuracy11 = 1 | |
| I0404 15:35:24.356736 9252 solver.cpp:406] Test net output #11: loss/accuracy12 = 1 | |
| I0404 15:35:24.356750 9252 solver.cpp:406] Test net output #12: loss/accuracy13 = 1 | |
| I0404 15:35:24.356761 9252 solver.cpp:406] Test net output #13: loss/accuracy14 = 1 | |
| I0404 15:35:24.356772 9252 solver.cpp:406] Test net output #14: loss/accuracy15 = 1 | |
| I0404 15:35:24.356783 9252 solver.cpp:406] Test net output #15: loss/accuracy16 = 1 | |
| I0404 15:35:24.356794 9252 solver.cpp:406] Test net output #16: loss/accuracy17 = 1 | |
| I0404 15:35:24.356806 9252 solver.cpp:406] Test net output #17: loss/accuracy18 = 1 | |
| I0404 15:35:24.356817 9252 solver.cpp:406] Test net output #18: loss/accuracy19 = 1 | |
| I0404 15:35:24.356827 9252 solver.cpp:406] Test net output #19: loss/accuracy20 = 1 | |
| I0404 15:35:24.356838 9252 solver.cpp:406] Test net output #20: loss/accuracy21 = 1 | |
| I0404 15:35:24.356849 9252 solver.cpp:406] Test net output #21: loss/accuracy22 = 1 | |
| I0404 15:35:24.356864 9252 solver.cpp:406] Test net output #22: loss/loss01 = 1.84413 (* 0.0454545 = 0.0838241 loss) | |
| I0404 15:35:24.356879 9252 solver.cpp:406] Test net output #23: loss/loss02 = 2.39827 (* 0.0454545 = 0.109012 loss) | |
| I0404 15:35:24.356892 9252 solver.cpp:406] Test net output #24: loss/loss03 = 2.5922 (* 0.0454545 = 0.117827 loss) | |
| I0404 15:35:24.356906 9252 solver.cpp:406] Test net output #25: loss/loss04 = 2.59523 (* 0.0454545 = 0.117965 loss) | |
| I0404 15:35:24.356920 9252 solver.cpp:406] Test net output #26: loss/loss05 = 2.32849 (* 0.0454545 = 0.105841 loss) | |
| I0404 15:35:24.356932 9252 solver.cpp:406] Test net output #27: loss/loss06 = 1.63742 (* 0.0454545 = 0.074428 loss) | |
| I0404 15:35:24.356945 9252 solver.cpp:406] Test net output #28: loss/loss07 = 0.421418 (* 0.0454545 = 0.0191554 loss) | |
| I0404 15:35:24.356958 9252 solver.cpp:406] Test net output #29: loss/loss08 = 0.185187 (* 0.0454545 = 0.00841757 loss) | |
| I0404 15:35:24.356972 9252 solver.cpp:406] Test net output #30: loss/loss09 = 0.0461865 (* 0.0454545 = 0.00209938 loss) | |
| I0404 15:35:24.356986 9252 solver.cpp:406] Test net output #31: loss/loss10 = 0.0206727 (* 0.0454545 = 0.000939666 loss) | |
| I0404 15:35:24.356999 9252 solver.cpp:406] Test net output #32: loss/loss11 = 1.21783e-05 (* 0.0454545 = 5.53561e-07 loss) | |
| I0404 15:35:24.357012 9252 solver.cpp:406] Test net output #33: loss/loss12 = 1.15271e-05 (* 0.0454545 = 5.23961e-07 loss) | |
| I0404 15:35:24.357026 9252 solver.cpp:406] Test net output #34: loss/loss13 = 1.04916e-05 (* 0.0454545 = 4.76892e-07 loss) | |
| I0404 15:35:24.357040 9252 solver.cpp:406] Test net output #35: loss/loss14 = 1.10241e-05 (* 0.0454545 = 5.01097e-07 loss) | |
| I0404 15:35:24.357054 9252 solver.cpp:406] Test net output #36: loss/loss15 = 1.19856e-05 (* 0.0454545 = 5.44798e-07 loss) | |
| I0404 15:35:24.357067 9252 solver.cpp:406] Test net output #37: loss/loss16 = 1.28341e-05 (* 0.0454545 = 5.8337e-07 loss) | |
| I0404 15:35:24.357081 9252 solver.cpp:406] Test net output #38: loss/loss17 = 9.3343e-06 (* 0.0454545 = 4.24286e-07 loss) | |
| I0404 15:35:24.357127 9252 solver.cpp:406] Test net output #39: loss/loss18 = 1.06035e-05 (* 0.0454545 = 4.81975e-07 loss) | |
| I0404 15:35:24.357142 9252 solver.cpp:406] Test net output #40: loss/loss19 = 1.05998e-05 (* 0.0454545 = 4.81807e-07 loss) | |
| I0404 15:35:24.357156 9252 solver.cpp:406] Test net output #41: loss/loss20 = 1.16291e-05 (* 0.0454545 = 5.28597e-07 loss) | |
| I0404 15:35:24.357170 9252 solver.cpp:406] Test net output #42: loss/loss21 = 1.11074e-05 (* 0.0454545 = 5.0488e-07 loss) | |
| I0404 15:35:24.357183 9252 solver.cpp:406] Test net output #43: loss/loss22 = 1.11227e-05 (* 0.0454545 = 5.05577e-07 loss) | |
| I0404 15:35:24.357195 9252 solver.cpp:406] Test net output #44: total_accuracy = 0.002 | |
| I0404 15:35:24.357206 9252 solver.cpp:406] Test net output #45: total_confidence = 0.00381643 | |
| I0404 15:35:24.391037 9252 solver.cpp:229] Iteration 70000, loss = 0.771609 | |
| I0404 15:35:24.391077 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.5625 | |
| I0404 15:35:24.391093 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.25 | |
| I0404 15:35:24.391105 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 15:35:24.391118 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 15:35:24.391129 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.40625 | |
| I0404 15:35:24.391140 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.59375 | |
| I0404 15:35:24.391152 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.875 | |
| I0404 15:35:24.391163 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 15:35:24.391175 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:35:24.391187 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:35:24.391198 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:35:24.391209 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:35:24.391222 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:35:24.391232 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:35:24.391243 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:35:24.391258 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:35:24.391270 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:35:24.391281 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:35:24.391293 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:35:24.391304 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:35:24.391315 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:35:24.391326 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:35:24.391340 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.50607 (* 0.0454545 = 0.0684579 loss) | |
| I0404 15:35:24.391355 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.59612 (* 0.0454545 = 0.118005 loss) | |
| I0404 15:35:24.391368 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.75548 (* 0.0454545 = 0.125249 loss) | |
| I0404 15:35:24.391381 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.85036 (* 0.0454545 = 0.129562 loss) | |
| I0404 15:35:24.391394 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.24903 (* 0.0454545 = 0.102228 loss) | |
| I0404 15:35:24.391407 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.49873 (* 0.0454545 = 0.0681242 loss) | |
| I0404 15:35:24.391420 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.544206 (* 0.0454545 = 0.0247367 loss) | |
| I0404 15:35:24.391434 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.229936 (* 0.0454545 = 0.0104517 loss) | |
| I0404 15:35:24.391448 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.107486 (* 0.0454545 = 0.00488574 loss) | |
| I0404 15:35:24.391480 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00173255 (* 0.0454545 = 7.87524e-05 loss) | |
| I0404 15:35:24.391496 9252 solver.cpp:245] Train net output #32: loss/loss11 = 9.40676e-06 (* 0.0454545 = 4.2758e-07 loss) | |
| I0404 15:35:24.391510 9252 solver.cpp:245] Train net output #33: loss/loss12 = 9.20017e-06 (* 0.0454545 = 4.1819e-07 loss) | |
| I0404 15:35:24.391525 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.8532e-06 (* 0.0454545 = 3.56964e-07 loss) | |
| I0404 15:35:24.391537 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.15772e-05 (* 0.0454545 = 5.26236e-07 loss) | |
| I0404 15:35:24.391551 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.11598e-05 (* 0.0454545 = 5.07264e-07 loss) | |
| I0404 15:35:24.391566 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.18714e-05 (* 0.0454545 = 5.39609e-07 loss) | |
| I0404 15:35:24.391578 9252 solver.cpp:245] Train net output #38: loss/loss17 = 8.30771e-06 (* 0.0454545 = 3.77623e-07 loss) | |
| I0404 15:35:24.391592 9252 solver.cpp:245] Train net output #39: loss/loss18 = 9.79056e-06 (* 0.0454545 = 4.45025e-07 loss) | |
| I0404 15:35:24.391607 9252 solver.cpp:245] Train net output #40: loss/loss19 = 8.17364e-06 (* 0.0454545 = 3.71529e-07 loss) | |
| I0404 15:35:24.391619 9252 solver.cpp:245] Train net output #41: loss/loss20 = 9.34722e-06 (* 0.0454545 = 4.24874e-07 loss) | |
| I0404 15:35:24.391633 9252 solver.cpp:245] Train net output #42: loss/loss21 = 8.99702e-06 (* 0.0454545 = 4.08955e-07 loss) | |
| I0404 15:35:24.391646 9252 solver.cpp:245] Train net output #43: loss/loss22 = 8.76229e-06 (* 0.0454545 = 3.98286e-07 loss) | |
| I0404 15:35:24.391659 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:35:24.391669 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00673391 | |
| I0404 15:35:24.391685 9252 sgd_solver.cpp:106] Iteration 70000, lr = 0.0093 | |
| I0404 15:36:35.263206 9252 solver.cpp:229] Iteration 70500, loss = 0.768389 | |
| I0404 15:36:35.263447 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.40625 | |
| I0404 15:36:35.263465 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.1875 | |
| I0404 15:36:35.263478 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.25 | |
| I0404 15:36:35.263490 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 15:36:35.263502 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.46875 | |
| I0404 15:36:35.263514 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.53125 | |
| I0404 15:36:35.263525 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:36:35.263537 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 15:36:35.263550 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 15:36:35.263561 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:36:35.263572 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:36:35.263584 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:36:35.263595 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:36:35.263607 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:36:35.263618 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:36:35.263630 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:36:35.263641 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:36:35.263653 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:36:35.263664 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:36:35.263676 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:36:35.263687 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:36:35.263700 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:36:35.263715 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.16312 (* 0.0454545 = 0.0983234 loss) | |
| I0404 15:36:35.263728 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.7487 (* 0.0454545 = 0.124941 loss) | |
| I0404 15:36:35.263742 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.07254 (* 0.0454545 = 0.139661 loss) | |
| I0404 15:36:35.263756 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.76622 (* 0.0454545 = 0.125737 loss) | |
| I0404 15:36:35.263770 9252 solver.cpp:245] Train net output #26: loss/loss05 = 1.98235 (* 0.0454545 = 0.0901067 loss) | |
| I0404 15:36:35.263783 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.71095 (* 0.0454545 = 0.0777704 loss) | |
| I0404 15:36:35.263802 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.02328 (* 0.0454545 = 0.0465128 loss) | |
| I0404 15:36:35.263815 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.122751 (* 0.0454545 = 0.0055796 loss) | |
| I0404 15:36:35.263829 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0783661 (* 0.0454545 = 0.0035621 loss) | |
| I0404 15:36:35.263844 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.114046 (* 0.0454545 = 0.00518391 loss) | |
| I0404 15:36:35.263857 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.07416e-05 (* 0.0454545 = 9.42799e-07 loss) | |
| I0404 15:36:35.263871 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.04363e-05 (* 0.0454545 = 9.28924e-07 loss) | |
| I0404 15:36:35.263885 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.16246e-05 (* 0.0454545 = 9.82937e-07 loss) | |
| I0404 15:36:35.263900 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.42032e-05 (* 0.0454545 = 1.10014e-06 loss) | |
| I0404 15:36:35.263913 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.33985e-05 (* 0.0454545 = 1.06357e-06 loss) | |
| I0404 15:36:35.263926 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.35957e-05 (* 0.0454545 = 1.07253e-06 loss) | |
| I0404 15:36:35.263941 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.87746e-05 (* 0.0454545 = 8.53389e-07 loss) | |
| I0404 15:36:35.263973 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.06524e-05 (* 0.0454545 = 9.38744e-07 loss) | |
| I0404 15:36:35.263989 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.9177e-05 (* 0.0454545 = 8.71684e-07 loss) | |
| I0404 15:36:35.264003 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.01866e-05 (* 0.0454545 = 9.17571e-07 loss) | |
| I0404 15:36:35.264016 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.97358e-05 (* 0.0454545 = 8.9708e-07 loss) | |
| I0404 15:36:35.264030 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.98513e-05 (* 0.0454545 = 9.02333e-07 loss) | |
| I0404 15:36:35.264042 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:36:35.264053 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00354426 | |
| I0404 15:36:35.264067 9252 sgd_solver.cpp:106] Iteration 70500, lr = 0.009295 | |
| I0404 15:37:46.287650 9252 solver.cpp:229] Iteration 71000, loss = 0.75769 | |
| I0404 15:37:46.287811 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.40625 | |
| I0404 15:37:46.287832 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.28125 | |
| I0404 15:37:46.287844 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 15:37:46.287856 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 15:37:46.287868 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 15:37:46.287880 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 15:37:46.287891 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:37:46.287907 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:37:46.287928 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:37:46.287942 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:37:46.287955 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:37:46.287966 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:37:46.287977 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:37:46.287989 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:37:46.288000 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:37:46.288012 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:37:46.288023 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:37:46.288034 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:37:46.288046 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:37:46.288058 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:37:46.288069 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:37:46.288080 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:37:46.288095 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.48392 (* 0.0454545 = 0.112905 loss) | |
| I0404 15:37:46.288110 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.46823 (* 0.0454545 = 0.112192 loss) | |
| I0404 15:37:46.288125 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.88867 (* 0.0454545 = 0.131303 loss) | |
| I0404 15:37:46.288137 9252 solver.cpp:245] Train net output #25: loss/loss04 = 3.03325 (* 0.0454545 = 0.137875 loss) | |
| I0404 15:37:46.288151 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.46362 (* 0.0454545 = 0.111983 loss) | |
| I0404 15:37:46.288164 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.44254 (* 0.0454545 = 0.111024 loss) | |
| I0404 15:37:46.288178 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.16937 (* 0.0454545 = 0.0531534 loss) | |
| I0404 15:37:46.288192 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.686241 (* 0.0454545 = 0.0311928 loss) | |
| I0404 15:37:46.288205 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.381637 (* 0.0454545 = 0.0173472 loss) | |
| I0404 15:37:46.288219 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0428511 (* 0.0454545 = 0.00194778 loss) | |
| I0404 15:37:46.288233 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.1906e-05 (* 0.0454545 = 1.90482e-06 loss) | |
| I0404 15:37:46.288249 9252 solver.cpp:245] Train net output #33: loss/loss12 = 4.06528e-05 (* 0.0454545 = 1.84786e-06 loss) | |
| I0404 15:37:46.288262 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.30045e-05 (* 0.0454545 = 1.95475e-06 loss) | |
| I0404 15:37:46.288275 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.32626e-05 (* 0.0454545 = 1.96648e-06 loss) | |
| I0404 15:37:46.288290 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.56397e-05 (* 0.0454545 = 2.07453e-06 loss) | |
| I0404 15:37:46.288303 9252 solver.cpp:245] Train net output #37: loss/loss16 = 4.93461e-05 (* 0.0454545 = 2.24301e-06 loss) | |
| I0404 15:37:46.288317 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.82756e-05 (* 0.0454545 = 1.7398e-06 loss) | |
| I0404 15:37:46.288346 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.81341e-05 (* 0.0454545 = 2.18792e-06 loss) | |
| I0404 15:37:46.288360 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.36955e-05 (* 0.0454545 = 1.98616e-06 loss) | |
| I0404 15:37:46.288374 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.445e-05 (* 0.0454545 = 2.02046e-06 loss) | |
| I0404 15:37:46.288391 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.34574e-05 (* 0.0454545 = 1.97534e-06 loss) | |
| I0404 15:37:46.288406 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.87749e-05 (* 0.0454545 = 1.7625e-06 loss) | |
| I0404 15:37:46.288419 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:37:46.288429 9252 solver.cpp:245] Train net output #45: total_confidence = 0.000557782 | |
| I0404 15:37:46.288444 9252 sgd_solver.cpp:106] Iteration 71000, lr = 0.00929 | |
| I0404 15:38:57.154203 9252 solver.cpp:229] Iteration 71500, loss = 0.758209 | |
| I0404 15:38:57.154335 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.53125 | |
| I0404 15:38:57.154355 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.375 | |
| I0404 15:38:57.154367 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.28125 | |
| I0404 15:38:57.154379 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 15:38:57.154392 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.375 | |
| I0404 15:38:57.154403 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 15:38:57.154415 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:38:57.154428 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.8125 | |
| I0404 15:38:57.154439 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:38:57.154450 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:38:57.154463 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:38:57.154474 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:38:57.154486 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:38:57.154497 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:38:57.154510 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:38:57.154521 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:38:57.154532 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:38:57.154543 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:38:57.154556 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:38:57.154567 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:38:57.154578 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:38:57.154590 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:38:57.154605 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.769 (* 0.0454545 = 0.0804093 loss) | |
| I0404 15:38:57.154620 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.26673 (* 0.0454545 = 0.103033 loss) | |
| I0404 15:38:57.154634 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.58753 (* 0.0454545 = 0.117615 loss) | |
| I0404 15:38:57.154647 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.86152 (* 0.0454545 = 0.130069 loss) | |
| I0404 15:38:57.154661 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.16259 (* 0.0454545 = 0.0982996 loss) | |
| I0404 15:38:57.154675 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.94763 (* 0.0454545 = 0.0885285 loss) | |
| I0404 15:38:57.154690 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.03137 (* 0.0454545 = 0.0468805 loss) | |
| I0404 15:38:57.154702 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.786834 (* 0.0454545 = 0.0357652 loss) | |
| I0404 15:38:57.154716 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.287582 (* 0.0454545 = 0.0130719 loss) | |
| I0404 15:38:57.154731 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.134053 (* 0.0454545 = 0.00609331 loss) | |
| I0404 15:38:57.154747 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.55748e-05 (* 0.0454545 = 1.61704e-06 loss) | |
| I0404 15:38:57.154762 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.39606e-05 (* 0.0454545 = 1.54366e-06 loss) | |
| I0404 15:38:57.154778 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.28164e-05 (* 0.0454545 = 1.49165e-06 loss) | |
| I0404 15:38:57.154791 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.70644e-05 (* 0.0454545 = 1.2302e-06 loss) | |
| I0404 15:38:57.154805 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.87027e-05 (* 0.0454545 = 1.30467e-06 loss) | |
| I0404 15:38:57.154819 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.03338e-05 (* 0.0454545 = 9.24262e-07 loss) | |
| I0404 15:38:57.154832 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.24452e-05 (* 0.0454545 = 1.02024e-06 loss) | |
| I0404 15:38:57.154865 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.43883e-05 (* 0.0454545 = 1.10856e-06 loss) | |
| I0404 15:38:57.154881 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.14498e-05 (* 0.0454545 = 9.74989e-07 loss) | |
| I0404 15:38:57.154894 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.83195e-05 (* 0.0454545 = 8.32705e-07 loss) | |
| I0404 15:38:57.154908 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.96573e-05 (* 0.0454545 = 8.93516e-07 loss) | |
| I0404 15:38:57.154922 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.5702e-05 (* 0.0454545 = 1.16827e-06 loss) | |
| I0404 15:38:57.154934 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:38:57.154945 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00869889 | |
| I0404 15:38:57.154959 9252 sgd_solver.cpp:106] Iteration 71500, lr = 0.009285 | |
| I0404 15:40:08.080710 9252 solver.cpp:229] Iteration 72000, loss = 0.748265 | |
| I0404 15:40:08.080864 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.40625 | |
| I0404 15:40:08.080885 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.28125 | |
| I0404 15:40:08.080899 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.25 | |
| I0404 15:40:08.080910 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 15:40:08.080922 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.40625 | |
| I0404 15:40:08.080935 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 15:40:08.080946 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.875 | |
| I0404 15:40:08.080958 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 15:40:08.080971 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:40:08.080981 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:40:08.080994 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:40:08.081006 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:40:08.081017 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:40:08.081028 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:40:08.081040 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:40:08.081051 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:40:08.081063 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:40:08.081074 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:40:08.081086 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:40:08.081106 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:40:08.081120 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:40:08.081130 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:40:08.081146 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.14311 (* 0.0454545 = 0.0974143 loss) | |
| I0404 15:40:08.081161 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.43195 (* 0.0454545 = 0.110543 loss) | |
| I0404 15:40:08.081174 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.67724 (* 0.0454545 = 0.121693 loss) | |
| I0404 15:40:08.081188 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.98703 (* 0.0454545 = 0.135774 loss) | |
| I0404 15:40:08.081202 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.11861 (* 0.0454545 = 0.0963002 loss) | |
| I0404 15:40:08.081215 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.7156 (* 0.0454545 = 0.0779816 loss) | |
| I0404 15:40:08.081229 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.598742 (* 0.0454545 = 0.0272155 loss) | |
| I0404 15:40:08.081243 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.352638 (* 0.0454545 = 0.016029 loss) | |
| I0404 15:40:08.081257 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.168999 (* 0.0454545 = 0.00768175 loss) | |
| I0404 15:40:08.081271 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0112914 (* 0.0454545 = 0.000513244 loss) | |
| I0404 15:40:08.081285 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.2583e-05 (* 0.0454545 = 5.71955e-07 loss) | |
| I0404 15:40:08.081300 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.22888e-05 (* 0.0454545 = 5.58583e-07 loss) | |
| I0404 15:40:08.081315 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.0016e-05 (* 0.0454545 = 4.55275e-07 loss) | |
| I0404 15:40:08.081328 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.29596e-05 (* 0.0454545 = 5.89072e-07 loss) | |
| I0404 15:40:08.081342 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.36526e-05 (* 0.0454545 = 6.20574e-07 loss) | |
| I0404 15:40:08.081357 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.47052e-05 (* 0.0454545 = 6.6842e-07 loss) | |
| I0404 15:40:08.081370 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.07649e-05 (* 0.0454545 = 4.89313e-07 loss) | |
| I0404 15:40:08.081401 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.10257e-05 (* 0.0454545 = 5.01167e-07 loss) | |
| I0404 15:40:08.081430 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.2233e-05 (* 0.0454545 = 5.56045e-07 loss) | |
| I0404 15:40:08.081449 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.20504e-05 (* 0.0454545 = 5.47744e-07 loss) | |
| I0404 15:40:08.081464 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.11226e-05 (* 0.0454545 = 5.05573e-07 loss) | |
| I0404 15:40:08.081478 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.09475e-05 (* 0.0454545 = 4.97612e-07 loss) | |
| I0404 15:40:08.081490 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.0625 | |
| I0404 15:40:08.081502 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0078492 | |
| I0404 15:40:08.081522 9252 sgd_solver.cpp:106] Iteration 72000, lr = 0.00928 | |
| I0404 15:41:19.365587 9252 solver.cpp:229] Iteration 72500, loss = 0.751353 | |
| I0404 15:41:19.365689 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.46875 | |
| I0404 15:41:19.365720 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.21875 | |
| I0404 15:41:19.365746 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 15:41:19.365769 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 15:41:19.365792 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 15:41:19.365815 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 15:41:19.365838 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:41:19.365862 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 15:41:19.365885 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:41:19.365912 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:41:19.365936 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:41:19.365957 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:41:19.365978 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:41:19.365998 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:41:19.366019 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:41:19.366040 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:41:19.366062 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:41:19.366083 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:41:19.366106 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:41:19.366127 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:41:19.366147 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:41:19.366168 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:41:19.366195 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.0611 (* 0.0454545 = 0.0936864 loss) | |
| I0404 15:41:19.366224 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.76198 (* 0.0454545 = 0.125545 loss) | |
| I0404 15:41:19.366250 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.64451 (* 0.0454545 = 0.120205 loss) | |
| I0404 15:41:19.366277 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.84619 (* 0.0454545 = 0.129372 loss) | |
| I0404 15:41:19.366307 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.43101 (* 0.0454545 = 0.1105 loss) | |
| I0404 15:41:19.366335 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.31869 (* 0.0454545 = 0.105395 loss) | |
| I0404 15:41:19.366363 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.04911 (* 0.0454545 = 0.0476866 loss) | |
| I0404 15:41:19.366389 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.210872 (* 0.0454545 = 0.0095851 loss) | |
| I0404 15:41:19.366413 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.162677 (* 0.0454545 = 0.00739442 loss) | |
| I0404 15:41:19.366441 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00286192 (* 0.0454545 = 0.000130087 loss) | |
| I0404 15:41:19.366466 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.79611e-06 (* 0.0454545 = 1.72551e-07 loss) | |
| I0404 15:41:19.366493 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.98398e-06 (* 0.0454545 = 1.35636e-07 loss) | |
| I0404 15:41:19.366520 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.05476e-06 (* 0.0454545 = 1.38853e-07 loss) | |
| I0404 15:41:19.366546 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.82379e-06 (* 0.0454545 = 1.28354e-07 loss) | |
| I0404 15:41:19.366574 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.47945e-06 (* 0.0454545 = 1.58157e-07 loss) | |
| I0404 15:41:19.366600 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.90202e-06 (* 0.0454545 = 1.3191e-07 loss) | |
| I0404 15:41:19.366626 9252 solver.cpp:245] Train net output #38: loss/loss17 = 3.24104e-06 (* 0.0454545 = 1.4732e-07 loss) | |
| I0404 15:41:19.366677 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.9579e-06 (* 0.0454545 = 1.3445e-07 loss) | |
| I0404 15:41:19.366704 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.71948e-06 (* 0.0454545 = 1.23613e-07 loss) | |
| I0404 15:41:19.366731 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.98026e-06 (* 0.0454545 = 1.35466e-07 loss) | |
| I0404 15:41:19.366765 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.83497e-06 (* 0.0454545 = 1.28862e-07 loss) | |
| I0404 15:41:19.366793 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.84987e-06 (* 0.0454545 = 1.29539e-07 loss) | |
| I0404 15:41:19.366817 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:41:19.366838 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00161058 | |
| I0404 15:41:19.366861 9252 sgd_solver.cpp:106] Iteration 72500, lr = 0.009275 | |
| I0404 15:42:30.357094 9252 solver.cpp:229] Iteration 73000, loss = 0.747079 | |
| I0404 15:42:30.357235 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.34375 | |
| I0404 15:42:30.357264 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.1875 | |
| I0404 15:42:30.357285 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 15:42:30.357306 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.15625 | |
| I0404 15:42:30.357328 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 15:42:30.357350 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 15:42:30.357372 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 15:42:30.357396 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 15:42:30.357434 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 15:42:30.357463 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:42:30.357486 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:42:30.357506 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:42:30.357529 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:42:30.357553 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:42:30.357573 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:42:30.357594 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:42:30.357615 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:42:30.357636 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:42:30.357657 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:42:30.357679 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:42:30.357699 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:42:30.357722 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:42:30.357753 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.12319 (* 0.0454545 = 0.0965087 loss) | |
| I0404 15:42:30.357780 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.67599 (* 0.0454545 = 0.121636 loss) | |
| I0404 15:42:30.357807 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.92818 (* 0.0454545 = 0.133099 loss) | |
| I0404 15:42:30.357833 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.98803 (* 0.0454545 = 0.13582 loss) | |
| I0404 15:42:30.357858 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.6266 (* 0.0454545 = 0.119391 loss) | |
| I0404 15:42:30.357883 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.2316 (* 0.0454545 = 0.101436 loss) | |
| I0404 15:42:30.357909 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.767534 (* 0.0454545 = 0.0348879 loss) | |
| I0404 15:42:30.357934 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.267262 (* 0.0454545 = 0.0121483 loss) | |
| I0404 15:42:30.357960 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0243208 (* 0.0454545 = 0.00110549 loss) | |
| I0404 15:42:30.357985 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00552251 (* 0.0454545 = 0.000251023 loss) | |
| I0404 15:42:30.358011 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.11617e-05 (* 0.0454545 = 5.07349e-07 loss) | |
| I0404 15:42:30.358034 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.04595e-05 (* 0.0454545 = 4.7543e-07 loss) | |
| I0404 15:42:30.358057 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.02023e-05 (* 0.0454545 = 4.63742e-07 loss) | |
| I0404 15:42:30.358083 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.00981e-05 (* 0.0454545 = 4.59002e-07 loss) | |
| I0404 15:42:30.358109 9252 solver.cpp:245] Train net output #36: loss/loss15 = 9.29516e-06 (* 0.0454545 = 4.22507e-07 loss) | |
| I0404 15:42:30.358135 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.68711e-06 (* 0.0454545 = 3.03959e-07 loss) | |
| I0404 15:42:30.358161 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.07612e-06 (* 0.0454545 = 2.76187e-07 loss) | |
| I0404 15:42:30.358207 9252 solver.cpp:245] Train net output #39: loss/loss18 = 8.73254e-06 (* 0.0454545 = 3.96934e-07 loss) | |
| I0404 15:42:30.358234 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.37789e-06 (* 0.0454545 = 2.89904e-07 loss) | |
| I0404 15:42:30.358265 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.84581e-06 (* 0.0454545 = 3.56628e-07 loss) | |
| I0404 15:42:30.358292 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.53661e-06 (* 0.0454545 = 3.42573e-07 loss) | |
| I0404 15:42:30.358320 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.24602e-06 (* 0.0454545 = 3.29364e-07 loss) | |
| I0404 15:42:30.358345 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 15:42:30.358371 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00522662 | |
| I0404 15:42:30.358394 9252 sgd_solver.cpp:106] Iteration 73000, lr = 0.00927 | |
| I0404 15:43:41.031035 9252 solver.cpp:229] Iteration 73500, loss = 0.744239 | |
| I0404 15:43:41.031162 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.34375 | |
| I0404 15:43:41.031191 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.3125 | |
| I0404 15:43:41.031214 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.1875 | |
| I0404 15:43:41.031239 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 15:43:41.031260 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 15:43:41.031281 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.375 | |
| I0404 15:43:41.031303 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 15:43:41.031324 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.8125 | |
| I0404 15:43:41.031345 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 15:43:41.031365 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.90625 | |
| I0404 15:43:41.031388 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:43:41.031409 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:43:41.031433 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:43:41.031456 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:43:41.031478 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:43:41.031499 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:43:41.031520 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:43:41.031541 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:43:41.031563 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:43:41.031584 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:43:41.031604 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:43:41.031625 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:43:41.031652 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.29818 (* 0.0454545 = 0.104463 loss) | |
| I0404 15:43:41.031679 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.63404 (* 0.0454545 = 0.119729 loss) | |
| I0404 15:43:41.031705 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.25439 (* 0.0454545 = 0.147927 loss) | |
| I0404 15:43:41.031730 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.80265 (* 0.0454545 = 0.127393 loss) | |
| I0404 15:43:41.031760 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.61482 (* 0.0454545 = 0.118856 loss) | |
| I0404 15:43:41.031786 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.57445 (* 0.0454545 = 0.11702 loss) | |
| I0404 15:43:41.031812 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.7973 (* 0.0454545 = 0.0816955 loss) | |
| I0404 15:43:41.031837 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.913218 (* 0.0454545 = 0.0415099 loss) | |
| I0404 15:43:41.031862 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.644776 (* 0.0454545 = 0.029308 loss) | |
| I0404 15:43:41.031888 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.730851 (* 0.0454545 = 0.0332205 loss) | |
| I0404 15:43:41.031915 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.88395e-05 (* 0.0454545 = 8.56343e-07 loss) | |
| I0404 15:43:41.031941 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.6362e-05 (* 0.0454545 = 7.43728e-07 loss) | |
| I0404 15:43:41.031967 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.91153e-05 (* 0.0454545 = 8.68879e-07 loss) | |
| I0404 15:43:41.031994 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.86162e-05 (* 0.0454545 = 8.46189e-07 loss) | |
| I0404 15:43:41.032021 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.66787e-05 (* 0.0454545 = 7.58122e-07 loss) | |
| I0404 15:43:41.032047 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.98605e-05 (* 0.0454545 = 9.02749e-07 loss) | |
| I0404 15:43:41.032073 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.69694e-05 (* 0.0454545 = 7.71336e-07 loss) | |
| I0404 15:43:41.032121 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.65446e-05 (* 0.0454545 = 7.52026e-07 loss) | |
| I0404 15:43:41.032151 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.71966e-05 (* 0.0454545 = 7.81664e-07 loss) | |
| I0404 15:43:41.032183 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.63471e-05 (* 0.0454545 = 7.43052e-07 loss) | |
| I0404 15:43:41.032213 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.9637e-05 (* 0.0454545 = 8.9259e-07 loss) | |
| I0404 15:43:41.032241 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.78933e-05 (* 0.0454545 = 8.13331e-07 loss) | |
| I0404 15:43:41.032264 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:43:41.032285 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0133566 | |
| I0404 15:43:41.032310 9252 sgd_solver.cpp:106] Iteration 73500, lr = 0.009265 | |
| I0404 15:44:51.793669 9252 solver.cpp:229] Iteration 74000, loss = 0.744752 | |
| I0404 15:44:51.793794 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.53125 | |
| I0404 15:44:51.793819 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.125 | |
| I0404 15:44:51.793833 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.375 | |
| I0404 15:44:51.793853 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 15:44:51.793869 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.40625 | |
| I0404 15:44:51.793880 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 15:44:51.793895 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 15:44:51.793915 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:44:51.793928 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:44:51.793941 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:44:51.793956 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:44:51.793975 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:44:51.793988 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:44:51.794000 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:44:51.794013 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:44:51.794033 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:44:51.794046 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:44:51.794057 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:44:51.794070 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:44:51.794083 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:44:51.794101 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:44:51.794114 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:44:51.794131 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.01065 (* 0.0454545 = 0.0913931 loss) | |
| I0404 15:44:51.794144 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.86 (* 0.0454545 = 0.13 loss) | |
| I0404 15:44:51.794159 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.5363 (* 0.0454545 = 0.115287 loss) | |
| I0404 15:44:51.794173 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.78733 (* 0.0454545 = 0.126697 loss) | |
| I0404 15:44:51.794186 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.14344 (* 0.0454545 = 0.0974293 loss) | |
| I0404 15:44:51.794200 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.01553 (* 0.0454545 = 0.0916151 loss) | |
| I0404 15:44:51.794214 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.855404 (* 0.0454545 = 0.038882 loss) | |
| I0404 15:44:51.794229 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.490002 (* 0.0454545 = 0.0222728 loss) | |
| I0404 15:44:51.794242 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.294045 (* 0.0454545 = 0.0133657 loss) | |
| I0404 15:44:51.794256 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.207746 (* 0.0454545 = 0.00944299 loss) | |
| I0404 15:44:51.794271 9252 solver.cpp:245] Train net output #32: loss/loss11 = 7.17503e-06 (* 0.0454545 = 3.26138e-07 loss) | |
| I0404 15:44:51.794286 9252 solver.cpp:245] Train net output #33: loss/loss12 = 6.87698e-06 (* 0.0454545 = 3.1259e-07 loss) | |
| I0404 15:44:51.794299 9252 solver.cpp:245] Train net output #34: loss/loss13 = 7.1564e-06 (* 0.0454545 = 3.25291e-07 loss) | |
| I0404 15:44:51.794313 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.41345e-06 (* 0.0454545 = 3.36975e-07 loss) | |
| I0404 15:44:51.794327 9252 solver.cpp:245] Train net output #36: loss/loss15 = 7.26444e-06 (* 0.0454545 = 3.30202e-07 loss) | |
| I0404 15:44:51.794342 9252 solver.cpp:245] Train net output #37: loss/loss16 = 7.93129e-06 (* 0.0454545 = 3.60513e-07 loss) | |
| I0404 15:44:51.794355 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.49701e-06 (* 0.0454545 = 2.95318e-07 loss) | |
| I0404 15:44:51.794385 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.41717e-06 (* 0.0454545 = 3.37144e-07 loss) | |
| I0404 15:44:51.794401 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.63484e-06 (* 0.0454545 = 3.01584e-07 loss) | |
| I0404 15:44:51.794414 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.1266e-06 (* 0.0454545 = 3.23936e-07 loss) | |
| I0404 15:44:51.794428 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.80622e-06 (* 0.0454545 = 3.09374e-07 loss) | |
| I0404 15:44:51.794442 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.54757e-06 (* 0.0454545 = 3.43072e-07 loss) | |
| I0404 15:44:51.794455 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:44:51.794466 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00684212 | |
| I0404 15:44:51.794479 9252 sgd_solver.cpp:106] Iteration 74000, lr = 0.00926 | |
| I0404 15:46:02.898759 9252 solver.cpp:229] Iteration 74500, loss = 0.735589 | |
| I0404 15:46:02.898999 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.375 | |
| I0404 15:46:02.899020 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.28125 | |
| I0404 15:46:02.899034 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.1875 | |
| I0404 15:46:02.899045 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 15:46:02.899057 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 15:46:02.899070 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 15:46:02.899081 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.65625 | |
| I0404 15:46:02.899092 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.84375 | |
| I0404 15:46:02.899104 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 15:46:02.899116 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.90625 | |
| I0404 15:46:02.899127 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:46:02.899139 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:46:02.899150 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:46:02.899163 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:46:02.899173 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:46:02.899185 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:46:02.899196 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:46:02.899209 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:46:02.899219 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:46:02.899230 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:46:02.899241 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:46:02.899253 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:46:02.899268 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.24556 (* 0.0454545 = 0.102071 loss) | |
| I0404 15:46:02.899283 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.36286 (* 0.0454545 = 0.107403 loss) | |
| I0404 15:46:02.899296 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.6973 (* 0.0454545 = 0.122605 loss) | |
| I0404 15:46:02.899310 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.92439 (* 0.0454545 = 0.132927 loss) | |
| I0404 15:46:02.899324 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.42874 (* 0.0454545 = 0.110397 loss) | |
| I0404 15:46:02.899338 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.20629 (* 0.0454545 = 0.100286 loss) | |
| I0404 15:46:02.899351 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.37159 (* 0.0454545 = 0.0623451 loss) | |
| I0404 15:46:02.899364 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.840503 (* 0.0454545 = 0.0382047 loss) | |
| I0404 15:46:02.899379 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.578202 (* 0.0454545 = 0.0262819 loss) | |
| I0404 15:46:02.899391 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.654075 (* 0.0454545 = 0.0297307 loss) | |
| I0404 15:46:02.899405 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.59967e-05 (* 0.0454545 = 1.18167e-06 loss) | |
| I0404 15:46:02.899420 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.20679e-05 (* 0.0454545 = 1.00309e-06 loss) | |
| I0404 15:46:02.899433 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.03208e-05 (* 0.0454545 = 9.23672e-07 loss) | |
| I0404 15:46:02.899447 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.41302e-05 (* 0.0454545 = 1.09683e-06 loss) | |
| I0404 15:46:02.899461 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.15928e-05 (* 0.0454545 = 9.8149e-07 loss) | |
| I0404 15:46:02.899476 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.41564e-05 (* 0.0454545 = 1.09802e-06 loss) | |
| I0404 15:46:02.899489 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.01456e-05 (* 0.0454545 = 9.15709e-07 loss) | |
| I0404 15:46:02.899520 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.19058e-05 (* 0.0454545 = 9.95719e-07 loss) | |
| I0404 15:46:02.899536 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.85119e-05 (* 0.0454545 = 8.41449e-07 loss) | |
| I0404 15:46:02.899549 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.24927e-05 (* 0.0454545 = 1.0224e-06 loss) | |
| I0404 15:46:02.899564 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.99557e-05 (* 0.0454545 = 9.07076e-07 loss) | |
| I0404 15:46:02.899577 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.16172e-05 (* 0.0454545 = 9.82598e-07 loss) | |
| I0404 15:46:02.899588 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:46:02.899600 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00141448 | |
| I0404 15:46:02.899613 9252 sgd_solver.cpp:106] Iteration 74500, lr = 0.009255 | |
| I0404 15:47:13.940065 9252 solver.cpp:229] Iteration 75000, loss = 0.739173 | |
| I0404 15:47:13.940242 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.5 | |
| I0404 15:47:13.940263 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.3125 | |
| I0404 15:47:13.940275 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 15:47:13.940287 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 15:47:13.940299 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.25 | |
| I0404 15:47:13.940310 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 15:47:13.940322 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 15:47:13.940335 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 15:47:13.940346 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:47:13.940357 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:47:13.940369 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:47:13.940382 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:47:13.940393 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:47:13.940404 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:47:13.940415 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:47:13.940428 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:47:13.940439 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:47:13.940450 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:47:13.940462 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:47:13.940474 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:47:13.940485 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:47:13.940496 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:47:13.940511 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.84121 (* 0.0454545 = 0.0836915 loss) | |
| I0404 15:47:13.940526 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.30078 (* 0.0454545 = 0.104581 loss) | |
| I0404 15:47:13.940541 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.74257 (* 0.0454545 = 0.124662 loss) | |
| I0404 15:47:13.940554 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.80131 (* 0.0454545 = 0.127332 loss) | |
| I0404 15:47:13.940567 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.68069 (* 0.0454545 = 0.121849 loss) | |
| I0404 15:47:13.940582 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.12938 (* 0.0454545 = 0.0967899 loss) | |
| I0404 15:47:13.940595 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.22975 (* 0.0454545 = 0.0558977 loss) | |
| I0404 15:47:13.940608 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.345634 (* 0.0454545 = 0.0157106 loss) | |
| I0404 15:47:13.940623 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.121066 (* 0.0454545 = 0.005503 loss) | |
| I0404 15:47:13.940636 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.134131 (* 0.0454545 = 0.00609686 loss) | |
| I0404 15:47:13.940651 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.5591e-05 (* 0.0454545 = 7.08683e-07 loss) | |
| I0404 15:47:13.940665 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.28751e-05 (* 0.0454545 = 5.85232e-07 loss) | |
| I0404 15:47:13.940678 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.20107e-05 (* 0.0454545 = 5.4594e-07 loss) | |
| I0404 15:47:13.940692 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.2972e-05 (* 0.0454545 = 5.89635e-07 loss) | |
| I0404 15:47:13.940706 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.1925e-05 (* 0.0454545 = 5.42046e-07 loss) | |
| I0404 15:47:13.940719 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.33221e-05 (* 0.0454545 = 6.05549e-07 loss) | |
| I0404 15:47:13.940733 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.09452e-05 (* 0.0454545 = 4.9751e-07 loss) | |
| I0404 15:47:13.940764 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.24987e-05 (* 0.0454545 = 5.68123e-07 loss) | |
| I0404 15:47:13.940780 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.08968e-05 (* 0.0454545 = 4.95308e-07 loss) | |
| I0404 15:47:13.940794 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.06658e-05 (* 0.0454545 = 4.84808e-07 loss) | |
| I0404 15:47:13.940807 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.09378e-05 (* 0.0454545 = 4.97171e-07 loss) | |
| I0404 15:47:13.940821 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.11203e-05 (* 0.0454545 = 5.05469e-07 loss) | |
| I0404 15:47:13.940834 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:47:13.940845 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00440852 | |
| I0404 15:47:13.940857 9252 sgd_solver.cpp:106] Iteration 75000, lr = 0.00925 | |
| I0404 15:48:25.054268 9252 solver.cpp:229] Iteration 75500, loss = 0.732183 | |
| I0404 15:48:25.054386 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.46875 | |
| I0404 15:48:25.054405 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.3125 | |
| I0404 15:48:25.054425 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.375 | |
| I0404 15:48:25.054437 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.3125 | |
| I0404 15:48:25.054450 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.3125 | |
| I0404 15:48:25.054461 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 15:48:25.054481 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 15:48:25.054493 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 15:48:25.054504 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 15:48:25.054517 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:48:25.054527 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:48:25.054539 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:48:25.054550 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:48:25.054561 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:48:25.054574 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:48:25.054584 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:48:25.054595 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:48:25.054607 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:48:25.054618 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:48:25.054630 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:48:25.054641 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:48:25.054652 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:48:25.054668 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.82985 (* 0.0454545 = 0.0831749 loss) | |
| I0404 15:48:25.054682 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.8256 (* 0.0454545 = 0.128436 loss) | |
| I0404 15:48:25.054697 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.65618 (* 0.0454545 = 0.120735 loss) | |
| I0404 15:48:25.054710 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.64803 (* 0.0454545 = 0.120365 loss) | |
| I0404 15:48:25.054723 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.30783 (* 0.0454545 = 0.104901 loss) | |
| I0404 15:48:25.054738 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.76365 (* 0.0454545 = 0.0801661 loss) | |
| I0404 15:48:25.054754 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.46804 (* 0.0454545 = 0.0667292 loss) | |
| I0404 15:48:25.054769 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.182724 (* 0.0454545 = 0.00830565 loss) | |
| I0404 15:48:25.054782 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0341097 (* 0.0454545 = 0.00155044 loss) | |
| I0404 15:48:25.054801 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00981973 (* 0.0454545 = 0.000446352 loss) | |
| I0404 15:48:25.054816 9252 solver.cpp:245] Train net output #32: loss/loss11 = 5.93818e-06 (* 0.0454545 = 2.69917e-07 loss) | |
| I0404 15:48:25.054829 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.77426e-06 (* 0.0454545 = 2.62466e-07 loss) | |
| I0404 15:48:25.054843 9252 solver.cpp:245] Train net output #34: loss/loss13 = 6.27719e-06 (* 0.0454545 = 2.85327e-07 loss) | |
| I0404 15:48:25.054857 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.32562e-06 (* 0.0454545 = 2.87528e-07 loss) | |
| I0404 15:48:25.054870 9252 solver.cpp:245] Train net output #36: loss/loss15 = 5.85994e-06 (* 0.0454545 = 2.66361e-07 loss) | |
| I0404 15:48:25.054884 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.98874e-06 (* 0.0454545 = 3.1767e-07 loss) | |
| I0404 15:48:25.054898 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.05526e-06 (* 0.0454545 = 2.29785e-07 loss) | |
| I0404 15:48:25.054937 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.49113e-06 (* 0.0454545 = 2.49597e-07 loss) | |
| I0404 15:48:25.054955 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.24898e-06 (* 0.0454545 = 2.3859e-07 loss) | |
| I0404 15:48:25.054970 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.60662e-06 (* 0.0454545 = 2.54846e-07 loss) | |
| I0404 15:48:25.054983 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.12817e-06 (* 0.0454545 = 2.78553e-07 loss) | |
| I0404 15:48:25.054997 9252 solver.cpp:245] Train net output #43: loss/loss22 = 5.31231e-06 (* 0.0454545 = 2.41469e-07 loss) | |
| I0404 15:48:25.055008 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 15:48:25.055021 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00331744 | |
| I0404 15:48:25.055033 9252 sgd_solver.cpp:106] Iteration 75500, lr = 0.009245 | |
| I0404 15:49:35.955621 9252 solver.cpp:229] Iteration 76000, loss = 0.731698 | |
| I0404 15:49:35.955729 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.34375 | |
| I0404 15:49:35.955747 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.21875 | |
| I0404 15:49:35.955760 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 15:49:35.955771 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.28125 | |
| I0404 15:49:35.955783 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.40625 | |
| I0404 15:49:35.955796 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 15:49:35.955807 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 15:49:35.955818 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 15:49:35.955831 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:49:35.955842 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:49:35.955854 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:49:35.955867 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:49:35.955878 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:49:35.955889 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:49:35.955904 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:49:35.955915 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:49:35.955927 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:49:35.955938 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:49:35.955950 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:49:35.955961 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:49:35.955973 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:49:35.955986 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:49:35.956001 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.94666 (* 0.0454545 = 0.0884846 loss) | |
| I0404 15:49:35.956014 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.69389 (* 0.0454545 = 0.122449 loss) | |
| I0404 15:49:35.956028 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.71921 (* 0.0454545 = 0.1236 loss) | |
| I0404 15:49:35.956043 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.5485 (* 0.0454545 = 0.115841 loss) | |
| I0404 15:49:35.956056 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.23773 (* 0.0454545 = 0.101715 loss) | |
| I0404 15:49:35.956069 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.64016 (* 0.0454545 = 0.0745526 loss) | |
| I0404 15:49:35.956084 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.849168 (* 0.0454545 = 0.0385986 loss) | |
| I0404 15:49:35.956097 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.370615 (* 0.0454545 = 0.0168461 loss) | |
| I0404 15:49:35.956111 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.262926 (* 0.0454545 = 0.0119512 loss) | |
| I0404 15:49:35.956125 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0125578 (* 0.0454545 = 0.000570808 loss) | |
| I0404 15:49:35.956140 9252 solver.cpp:245] Train net output #32: loss/loss11 = 8.63535e-06 (* 0.0454545 = 3.92516e-07 loss) | |
| I0404 15:49:35.956156 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.74871e-06 (* 0.0454545 = 3.52214e-07 loss) | |
| I0404 15:49:35.956171 9252 solver.cpp:245] Train net output #34: loss/loss13 = 8.46771e-06 (* 0.0454545 = 3.84896e-07 loss) | |
| I0404 15:49:35.956185 9252 solver.cpp:245] Train net output #35: loss/loss14 = 8.24046e-06 (* 0.0454545 = 3.74566e-07 loss) | |
| I0404 15:49:35.956199 9252 solver.cpp:245] Train net output #36: loss/loss15 = 9.17552e-06 (* 0.0454545 = 4.17069e-07 loss) | |
| I0404 15:49:35.956213 9252 solver.cpp:245] Train net output #37: loss/loss16 = 8.02811e-06 (* 0.0454545 = 3.64914e-07 loss) | |
| I0404 15:49:35.956226 9252 solver.cpp:245] Train net output #38: loss/loss17 = 8.27027e-06 (* 0.0454545 = 3.75921e-07 loss) | |
| I0404 15:49:35.956257 9252 solver.cpp:245] Train net output #39: loss/loss18 = 8.01693e-06 (* 0.0454545 = 3.64406e-07 loss) | |
| I0404 15:49:35.956274 9252 solver.cpp:245] Train net output #40: loss/loss19 = 7.65185e-06 (* 0.0454545 = 3.47811e-07 loss) | |
| I0404 15:49:35.956286 9252 solver.cpp:245] Train net output #41: loss/loss20 = 8.35968e-06 (* 0.0454545 = 3.79986e-07 loss) | |
| I0404 15:49:35.956301 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.27187e-06 (* 0.0454545 = 3.30539e-07 loss) | |
| I0404 15:49:35.956315 9252 solver.cpp:245] Train net output #43: loss/loss22 = 8.27771e-06 (* 0.0454545 = 3.7626e-07 loss) | |
| I0404 15:49:35.956326 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:49:35.956338 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00472267 | |
| I0404 15:49:35.956352 9252 sgd_solver.cpp:106] Iteration 76000, lr = 0.00924 | |
| I0404 15:50:47.155887 9252 solver.cpp:229] Iteration 76500, loss = 0.723767 | |
| I0404 15:50:47.156028 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.40625 | |
| I0404 15:50:47.156047 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.5 | |
| I0404 15:50:47.156059 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 15:50:47.156072 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 15:50:47.156083 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.0625 | |
| I0404 15:50:47.156095 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.34375 | |
| I0404 15:50:47.156107 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 15:50:47.156119 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 15:50:47.156131 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:50:47.156142 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:50:47.156154 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:50:47.156167 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:50:47.156177 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:50:47.156188 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:50:47.156200 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:50:47.156211 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:50:47.156224 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:50:47.156234 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:50:47.156246 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:50:47.156257 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:50:47.156270 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:50:47.156280 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:50:47.156296 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.328 (* 0.0454545 = 0.105818 loss) | |
| I0404 15:50:47.156311 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.09872 (* 0.0454545 = 0.0953965 loss) | |
| I0404 15:50:47.156324 9252 solver.cpp:245] Train net output #24: loss/loss03 = 3.05253 (* 0.0454545 = 0.138751 loss) | |
| I0404 15:50:47.156337 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.96241 (* 0.0454545 = 0.134655 loss) | |
| I0404 15:50:47.156352 9252 solver.cpp:245] Train net output #26: loss/loss05 = 3.0786 (* 0.0454545 = 0.139937 loss) | |
| I0404 15:50:47.156364 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.79138 (* 0.0454545 = 0.126881 loss) | |
| I0404 15:50:47.156378 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.26053 (* 0.0454545 = 0.0572968 loss) | |
| I0404 15:50:47.156393 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.39885 (* 0.0454545 = 0.0181295 loss) | |
| I0404 15:50:47.156407 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.162596 (* 0.0454545 = 0.00739072 loss) | |
| I0404 15:50:47.156421 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0185252 (* 0.0454545 = 0.000842055 loss) | |
| I0404 15:50:47.156435 9252 solver.cpp:245] Train net output #32: loss/loss11 = 8.77699e-06 (* 0.0454545 = 3.98954e-07 loss) | |
| I0404 15:50:47.156450 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.71896e-06 (* 0.0454545 = 3.50862e-07 loss) | |
| I0404 15:50:47.156462 9252 solver.cpp:245] Train net output #34: loss/loss13 = 8.88131e-06 (* 0.0454545 = 4.03696e-07 loss) | |
| I0404 15:50:47.156476 9252 solver.cpp:245] Train net output #35: loss/loss14 = 7.4768e-06 (* 0.0454545 = 3.39855e-07 loss) | |
| I0404 15:50:47.156491 9252 solver.cpp:245] Train net output #36: loss/loss15 = 9.12351e-06 (* 0.0454545 = 4.14705e-07 loss) | |
| I0404 15:50:47.156504 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.88074e-06 (* 0.0454545 = 3.12761e-07 loss) | |
| I0404 15:50:47.156517 9252 solver.cpp:245] Train net output #38: loss/loss17 = 6.96642e-06 (* 0.0454545 = 3.16655e-07 loss) | |
| I0404 15:50:47.156548 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.68172e-06 (* 0.0454545 = 3.49169e-07 loss) | |
| I0404 15:50:47.156563 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.90095e-06 (* 0.0454545 = 2.68225e-07 loss) | |
| I0404 15:50:47.156577 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.07075e-06 (* 0.0454545 = 3.21398e-07 loss) | |
| I0404 15:50:47.156591 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.48957e-06 (* 0.0454545 = 2.94981e-07 loss) | |
| I0404 15:50:47.156605 9252 solver.cpp:245] Train net output #43: loss/loss22 = 6.41133e-06 (* 0.0454545 = 2.91424e-07 loss) | |
| I0404 15:50:47.156617 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:50:47.156628 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00183599 | |
| I0404 15:50:47.156642 9252 sgd_solver.cpp:106] Iteration 76500, lr = 0.009235 | |
| I0404 15:51:58.604284 9252 solver.cpp:229] Iteration 77000, loss = 0.725442 | |
| I0404 15:51:58.604461 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.625 | |
| I0404 15:51:58.604482 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.3125 | |
| I0404 15:51:58.604496 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.03125 | |
| I0404 15:51:58.604508 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 15:51:58.604521 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 15:51:58.604532 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 15:51:58.604543 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.90625 | |
| I0404 15:51:58.604557 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 15:51:58.604568 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:51:58.604579 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:51:58.604591 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:51:58.604603 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:51:58.604614 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:51:58.604625 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:51:58.604636 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:51:58.604648 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:51:58.604660 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:51:58.604671 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:51:58.604682 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:51:58.604693 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:51:58.604706 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:51:58.604717 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:51:58.604732 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.62439 (* 0.0454545 = 0.0738358 loss) | |
| I0404 15:51:58.604748 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.34193 (* 0.0454545 = 0.106451 loss) | |
| I0404 15:51:58.604763 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.90231 (* 0.0454545 = 0.131923 loss) | |
| I0404 15:51:58.604778 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.7131 (* 0.0454545 = 0.123323 loss) | |
| I0404 15:51:58.604791 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.50137 (* 0.0454545 = 0.113699 loss) | |
| I0404 15:51:58.604804 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.9106 (* 0.0454545 = 0.0868455 loss) | |
| I0404 15:51:58.604818 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.4582 (* 0.0454545 = 0.0208273 loss) | |
| I0404 15:51:58.604832 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.124123 (* 0.0454545 = 0.00564196 loss) | |
| I0404 15:51:58.604846 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0759448 (* 0.0454545 = 0.00345204 loss) | |
| I0404 15:51:58.604861 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00886082 (* 0.0454545 = 0.000402765 loss) | |
| I0404 15:51:58.604874 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.40223e-05 (* 0.0454545 = 1.09192e-06 loss) | |
| I0404 15:51:58.604888 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.18677e-05 (* 0.0454545 = 1.44853e-06 loss) | |
| I0404 15:51:58.604902 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.89816e-05 (* 0.0454545 = 1.31735e-06 loss) | |
| I0404 15:51:58.604915 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.72573e-05 (* 0.0454545 = 1.23897e-06 loss) | |
| I0404 15:51:58.604929 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.42407e-05 (* 0.0454545 = 1.10185e-06 loss) | |
| I0404 15:51:58.604943 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.30981e-05 (* 0.0454545 = 1.04991e-06 loss) | |
| I0404 15:51:58.604956 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.47106e-05 (* 0.0454545 = 1.12321e-06 loss) | |
| I0404 15:51:58.604984 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.37453e-05 (* 0.0454545 = 1.07933e-06 loss) | |
| I0404 15:51:58.605000 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.97726e-05 (* 0.0454545 = 8.98756e-07 loss) | |
| I0404 15:51:58.605015 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.68439e-05 (* 0.0454545 = 1.22018e-06 loss) | |
| I0404 15:51:58.605027 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.98619e-05 (* 0.0454545 = 9.02812e-07 loss) | |
| I0404 15:51:58.605041 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.69878e-05 (* 0.0454545 = 1.22672e-06 loss) | |
| I0404 15:51:58.605053 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:51:58.605064 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00246807 | |
| I0404 15:51:58.605078 9252 sgd_solver.cpp:106] Iteration 77000, lr = 0.00923 | |
| I0404 15:53:09.993226 9252 solver.cpp:229] Iteration 77500, loss = 0.717383 | |
| I0404 15:53:09.993381 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.65625 | |
| I0404 15:53:09.993403 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.25 | |
| I0404 15:53:09.993415 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 15:53:09.993427 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.28125 | |
| I0404 15:53:09.993439 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 15:53:09.993451 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.59375 | |
| I0404 15:53:09.993463 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 15:53:09.993475 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 15:53:09.993486 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:53:09.993499 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:53:09.993525 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:53:09.993536 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:53:09.993548 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:53:09.993561 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:53:09.993571 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:53:09.993582 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:53:09.993594 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:53:09.993605 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:53:09.993616 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:53:09.993628 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:53:09.993639 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:53:09.993651 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:53:09.993675 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.44488 (* 0.0454545 = 0.0656762 loss) | |
| I0404 15:53:09.993690 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.35093 (* 0.0454545 = 0.10686 loss) | |
| I0404 15:53:09.993705 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.83046 (* 0.0454545 = 0.128657 loss) | |
| I0404 15:53:09.993729 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.55173 (* 0.0454545 = 0.115988 loss) | |
| I0404 15:53:09.993748 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.24105 (* 0.0454545 = 0.101866 loss) | |
| I0404 15:53:09.993763 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.56946 (* 0.0454545 = 0.0713393 loss) | |
| I0404 15:53:09.993777 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.698066 (* 0.0454545 = 0.0317303 loss) | |
| I0404 15:53:09.993791 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.167628 (* 0.0454545 = 0.00761946 loss) | |
| I0404 15:53:09.993805 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.166084 (* 0.0454545 = 0.00754928 loss) | |
| I0404 15:53:09.993820 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.000507881 (* 0.0454545 = 2.30855e-05 loss) | |
| I0404 15:53:09.993834 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.22829e-06 (* 0.0454545 = 1.92195e-07 loss) | |
| I0404 15:53:09.993849 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.88927e-06 (* 0.0454545 = 1.76785e-07 loss) | |
| I0404 15:53:09.993861 9252 solver.cpp:245] Train net output #34: loss/loss13 = 3.61731e-06 (* 0.0454545 = 1.64423e-07 loss) | |
| I0404 15:53:09.993875 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.79614e-06 (* 0.0454545 = 1.72552e-07 loss) | |
| I0404 15:53:09.993890 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.07183e-06 (* 0.0454545 = 1.85083e-07 loss) | |
| I0404 15:53:09.993903 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.94143e-06 (* 0.0454545 = 1.79156e-07 loss) | |
| I0404 15:53:09.993916 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.88714e-06 (* 0.0454545 = 1.31234e-07 loss) | |
| I0404 15:53:09.993949 9252 solver.cpp:245] Train net output #39: loss/loss18 = 3.69183e-06 (* 0.0454545 = 1.6781e-07 loss) | |
| I0404 15:53:09.993964 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.66947e-06 (* 0.0454545 = 1.66794e-07 loss) | |
| I0404 15:53:09.993978 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.60458e-06 (* 0.0454545 = 2.09299e-07 loss) | |
| I0404 15:53:09.993991 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.85203e-06 (* 0.0454545 = 1.75092e-07 loss) | |
| I0404 15:53:09.994005 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.73281e-06 (* 0.0454545 = 1.69673e-07 loss) | |
| I0404 15:53:09.994017 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 15:53:09.994030 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00380333 | |
| I0404 15:53:09.994043 9252 sgd_solver.cpp:106] Iteration 77500, lr = 0.009225 | |
| I0404 15:54:21.686728 9252 solver.cpp:229] Iteration 78000, loss = 0.711473 | |
| I0404 15:54:21.686857 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.5625 | |
| I0404 15:54:21.686887 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.25 | |
| I0404 15:54:21.686913 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.28125 | |
| I0404 15:54:21.686934 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 15:54:21.686955 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.46875 | |
| I0404 15:54:21.686977 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 15:54:21.687000 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.90625 | |
| I0404 15:54:21.687023 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 15:54:21.687048 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 15:54:21.687072 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:54:21.687093 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:54:21.687115 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:54:21.687136 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:54:21.687157 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:54:21.687180 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:54:21.687199 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:54:21.687221 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:54:21.687242 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:54:21.687263 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:54:21.687283 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:54:21.687304 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:54:21.687326 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:54:21.687352 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.48179 (* 0.0454545 = 0.0673539 loss) | |
| I0404 15:54:21.687379 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.53774 (* 0.0454545 = 0.115352 loss) | |
| I0404 15:54:21.687405 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.86927 (* 0.0454545 = 0.130422 loss) | |
| I0404 15:54:21.687430 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.62466 (* 0.0454545 = 0.119303 loss) | |
| I0404 15:54:21.687456 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.13849 (* 0.0454545 = 0.097204 loss) | |
| I0404 15:54:21.687484 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.84249 (* 0.0454545 = 0.0837495 loss) | |
| I0404 15:54:21.687513 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.477367 (* 0.0454545 = 0.0216985 loss) | |
| I0404 15:54:21.687539 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.255138 (* 0.0454545 = 0.0115972 loss) | |
| I0404 15:54:21.687566 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0271553 (* 0.0454545 = 0.00123433 loss) | |
| I0404 15:54:21.687592 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00980053 (* 0.0454545 = 0.000445479 loss) | |
| I0404 15:54:21.687618 9252 solver.cpp:245] Train net output #32: loss/loss11 = 4.66413e-06 (* 0.0454545 = 2.12006e-07 loss) | |
| I0404 15:54:21.687645 9252 solver.cpp:245] Train net output #33: loss/loss12 = 3.80357e-06 (* 0.0454545 = 1.72889e-07 loss) | |
| I0404 15:54:21.687671 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.35867e-06 (* 0.0454545 = 1.98121e-07 loss) | |
| I0404 15:54:21.687698 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.52629e-06 (* 0.0454545 = 2.0574e-07 loss) | |
| I0404 15:54:21.687724 9252 solver.cpp:245] Train net output #36: loss/loss15 = 3.6322e-06 (* 0.0454545 = 1.651e-07 loss) | |
| I0404 15:54:21.687757 9252 solver.cpp:245] Train net output #37: loss/loss16 = 3.79239e-06 (* 0.0454545 = 1.72381e-07 loss) | |
| I0404 15:54:21.687783 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.1426e-06 (* 0.0454545 = 1.883e-07 loss) | |
| I0404 15:54:21.687834 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.17983e-06 (* 0.0454545 = 1.89992e-07 loss) | |
| I0404 15:54:21.687861 9252 solver.cpp:245] Train net output #40: loss/loss19 = 3.75886e-06 (* 0.0454545 = 1.70857e-07 loss) | |
| I0404 15:54:21.687893 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.06808e-06 (* 0.0454545 = 1.84913e-07 loss) | |
| I0404 15:54:21.687922 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.16494e-06 (* 0.0454545 = 1.89316e-07 loss) | |
| I0404 15:54:21.687947 9252 solver.cpp:245] Train net output #43: loss/loss22 = 3.89298e-06 (* 0.0454545 = 1.76954e-07 loss) | |
| I0404 15:54:21.687969 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:54:21.687990 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00403256 | |
| I0404 15:54:21.688014 9252 sgd_solver.cpp:106] Iteration 78000, lr = 0.00922 | |
| I0404 15:55:32.321259 9252 solver.cpp:229] Iteration 78500, loss = 0.711916 | |
| I0404 15:55:32.321375 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.4375 | |
| I0404 15:55:32.321408 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.3125 | |
| I0404 15:55:32.321456 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 15:55:32.321481 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.125 | |
| I0404 15:55:32.321506 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 15:55:32.321532 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.53125 | |
| I0404 15:55:32.321554 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:55:32.321578 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 15:55:32.321599 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:55:32.321621 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:55:32.321643 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:55:32.321665 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:55:32.321686 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:55:32.321708 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:55:32.321729 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:55:32.321755 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:55:32.321777 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:55:32.321800 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:55:32.321825 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:55:32.321847 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:55:32.321869 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:55:32.321892 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:55:32.321918 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.0263 (* 0.0454545 = 0.0921044 loss) | |
| I0404 15:55:32.321945 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.36287 (* 0.0454545 = 0.107403 loss) | |
| I0404 15:55:32.321971 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.92176 (* 0.0454545 = 0.132807 loss) | |
| I0404 15:55:32.321997 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.92627 (* 0.0454545 = 0.133012 loss) | |
| I0404 15:55:32.322024 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.43881 (* 0.0454545 = 0.110855 loss) | |
| I0404 15:55:32.322049 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.95135 (* 0.0454545 = 0.0886977 loss) | |
| I0404 15:55:32.322077 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.983446 (* 0.0454545 = 0.0447021 loss) | |
| I0404 15:55:32.322103 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.319936 (* 0.0454545 = 0.0145425 loss) | |
| I0404 15:55:32.322129 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.123417 (* 0.0454545 = 0.00560984 loss) | |
| I0404 15:55:32.322156 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00309437 (* 0.0454545 = 0.000140653 loss) | |
| I0404 15:55:32.322182 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.36911e-05 (* 0.0454545 = 6.22322e-07 loss) | |
| I0404 15:55:32.322209 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.32739e-05 (* 0.0454545 = 6.0336e-07 loss) | |
| I0404 15:55:32.322235 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.07217e-05 (* 0.0454545 = 4.87352e-07 loss) | |
| I0404 15:55:32.322263 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.13141e-05 (* 0.0454545 = 5.14279e-07 loss) | |
| I0404 15:55:32.322288 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.18953e-05 (* 0.0454545 = 5.40697e-07 loss) | |
| I0404 15:55:32.322314 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.05728e-05 (* 0.0454545 = 4.80581e-07 loss) | |
| I0404 15:55:32.322341 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.13031e-05 (* 0.0454545 = 5.13776e-07 loss) | |
| I0404 15:55:32.322394 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.20742e-05 (* 0.0454545 = 5.48827e-07 loss) | |
| I0404 15:55:32.322424 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.1806e-05 (* 0.0454545 = 5.36635e-07 loss) | |
| I0404 15:55:32.322453 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.16644e-05 (* 0.0454545 = 5.302e-07 loss) | |
| I0404 15:55:32.322479 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.12472e-05 (* 0.0454545 = 5.11236e-07 loss) | |
| I0404 15:55:32.322505 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.26164e-05 (* 0.0454545 = 5.73475e-07 loss) | |
| I0404 15:55:32.322528 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 15:55:32.322551 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00659282 | |
| I0404 15:55:32.322574 9252 sgd_solver.cpp:106] Iteration 78500, lr = 0.009215 | |
| I0404 15:56:43.103029 9252 solver.cpp:229] Iteration 79000, loss = 0.709802 | |
| I0404 15:56:43.103345 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.71875 | |
| I0404 15:56:43.103366 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.46875 | |
| I0404 15:56:43.103379 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 15:56:43.103392 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.28125 | |
| I0404 15:56:43.103404 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.375 | |
| I0404 15:56:43.103415 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.53125 | |
| I0404 15:56:43.103427 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 15:56:43.103440 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 1 | |
| I0404 15:56:43.103451 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:56:43.103463 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:56:43.103474 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:56:43.103487 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:56:43.103497 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:56:43.103508 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:56:43.103519 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:56:43.103530 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:56:43.103543 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:56:43.103554 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:56:43.103564 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:56:43.103575 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:56:43.103587 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:56:43.103598 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:56:43.103615 9252 solver.cpp:245] Train net output #22: loss/loss01 = 0.88756 (* 0.0454545 = 0.0403436 loss) | |
| I0404 15:56:43.103628 9252 solver.cpp:245] Train net output #23: loss/loss02 = 1.91715 (* 0.0454545 = 0.0871431 loss) | |
| I0404 15:56:43.103642 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.64989 (* 0.0454545 = 0.12045 loss) | |
| I0404 15:56:43.103657 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.31172 (* 0.0454545 = 0.105078 loss) | |
| I0404 15:56:43.103669 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.15232 (* 0.0454545 = 0.097833 loss) | |
| I0404 15:56:43.103683 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.76366 (* 0.0454545 = 0.0801665 loss) | |
| I0404 15:56:43.103696 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.843371 (* 0.0454545 = 0.0383351 loss) | |
| I0404 15:56:43.103714 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.123824 (* 0.0454545 = 0.00562838 loss) | |
| I0404 15:56:43.103729 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.126149 (* 0.0454545 = 0.00573407 loss) | |
| I0404 15:56:43.103742 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0193393 (* 0.0454545 = 0.00087906 loss) | |
| I0404 15:56:43.103756 9252 solver.cpp:245] Train net output #32: loss/loss11 = 5.21924e-06 (* 0.0454545 = 2.37238e-07 loss) | |
| I0404 15:56:43.103770 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.79298e-06 (* 0.0454545 = 2.63317e-07 loss) | |
| I0404 15:56:43.103785 9252 solver.cpp:245] Train net output #34: loss/loss13 = 4.34376e-06 (* 0.0454545 = 1.97444e-07 loss) | |
| I0404 15:56:43.103804 9252 solver.cpp:245] Train net output #35: loss/loss14 = 4.77592e-06 (* 0.0454545 = 2.17087e-07 loss) | |
| I0404 15:56:43.103818 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.76847e-06 (* 0.0454545 = 2.16749e-07 loss) | |
| I0404 15:56:43.103832 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.53593e-06 (* 0.0454545 = 2.51633e-07 loss) | |
| I0404 15:56:43.103845 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.47415e-06 (* 0.0454545 = 2.0337e-07 loss) | |
| I0404 15:56:43.103880 9252 solver.cpp:245] Train net output #39: loss/loss18 = 4.69395e-06 (* 0.0454545 = 2.13361e-07 loss) | |
| I0404 15:56:43.103895 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.42945e-06 (* 0.0454545 = 2.01339e-07 loss) | |
| I0404 15:56:43.103909 9252 solver.cpp:245] Train net output #41: loss/loss20 = 4.25436e-06 (* 0.0454545 = 1.9338e-07 loss) | |
| I0404 15:56:43.103924 9252 solver.cpp:245] Train net output #42: loss/loss21 = 3.77005e-06 (* 0.0454545 = 1.71366e-07 loss) | |
| I0404 15:56:43.103937 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.34004e-06 (* 0.0454545 = 1.97275e-07 loss) | |
| I0404 15:56:43.103950 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:56:43.103963 9252 solver.cpp:245] Train net output #45: total_confidence = 0.009644 | |
| I0404 15:56:43.103977 9252 sgd_solver.cpp:106] Iteration 79000, lr = 0.00921 | |
| I0404 15:57:54.436477 9252 solver.cpp:229] Iteration 79500, loss = 0.706611 | |
| I0404 15:57:54.436583 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.53125 | |
| I0404 15:57:54.436601 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.3125 | |
| I0404 15:57:54.436614 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 15:57:54.436626 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.34375 | |
| I0404 15:57:54.436637 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.21875 | |
| I0404 15:57:54.436650 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.53125 | |
| I0404 15:57:54.436661 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 15:57:54.436672 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 15:57:54.436684 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 15:57:54.436697 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 15:57:54.436707 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:57:54.436719 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:57:54.436730 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:57:54.436741 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:57:54.436753 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:57:54.436764 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:57:54.436775 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:57:54.436787 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:57:54.436799 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:57:54.436810 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:57:54.436821 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:57:54.436832 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:57:54.436848 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.97354 (* 0.0454545 = 0.0897062 loss) | |
| I0404 15:57:54.436862 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.56046 (* 0.0454545 = 0.116384 loss) | |
| I0404 15:57:54.436877 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.92521 (* 0.0454545 = 0.132964 loss) | |
| I0404 15:57:54.436890 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.50437 (* 0.0454545 = 0.113835 loss) | |
| I0404 15:57:54.436907 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.90472 (* 0.0454545 = 0.132033 loss) | |
| I0404 15:57:54.436921 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.7432 (* 0.0454545 = 0.0792365 loss) | |
| I0404 15:57:54.436934 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.885317 (* 0.0454545 = 0.0402417 loss) | |
| I0404 15:57:54.436949 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.217094 (* 0.0454545 = 0.00986793 loss) | |
| I0404 15:57:54.436964 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.0791516 (* 0.0454545 = 0.0035978 loss) | |
| I0404 15:57:54.436977 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00558733 (* 0.0454545 = 0.00025397 loss) | |
| I0404 15:57:54.436992 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.2932e-06 (* 0.0454545 = 1.49691e-07 loss) | |
| I0404 15:57:54.437006 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.91695e-06 (* 0.0454545 = 1.32589e-07 loss) | |
| I0404 15:57:54.437021 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.77165e-06 (* 0.0454545 = 1.25984e-07 loss) | |
| I0404 15:57:54.437034 9252 solver.cpp:245] Train net output #35: loss/loss14 = 3.24479e-06 (* 0.0454545 = 1.4749e-07 loss) | |
| I0404 15:57:54.437048 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.68225e-06 (* 0.0454545 = 1.2192e-07 loss) | |
| I0404 15:57:54.437062 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.96911e-06 (* 0.0454545 = 1.3496e-07 loss) | |
| I0404 15:57:54.437077 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.27618e-06 (* 0.0454545 = 1.03463e-07 loss) | |
| I0404 15:57:54.437108 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.56676e-06 (* 0.0454545 = 1.16671e-07 loss) | |
| I0404 15:57:54.437122 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.80519e-06 (* 0.0454545 = 1.27509e-07 loss) | |
| I0404 15:57:54.437136 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.57421e-06 (* 0.0454545 = 1.1701e-07 loss) | |
| I0404 15:57:54.437150 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.50715e-06 (* 0.0454545 = 1.13962e-07 loss) | |
| I0404 15:57:54.437165 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.55931e-06 (* 0.0454545 = 1.16332e-07 loss) | |
| I0404 15:57:54.437176 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:57:54.437187 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00534914 | |
| I0404 15:57:54.437201 9252 sgd_solver.cpp:106] Iteration 79500, lr = 0.009205 | |
| I0404 15:59:05.247905 9252 solver.cpp:338] Iteration 80000, Testing net (#0) | |
| I0404 15:59:13.356449 9252 solver.cpp:393] Test loss: 0.656696 | |
| I0404 15:59:13.356494 9252 solver.cpp:406] Test net output #0: loss/accuracy01 = 0.513 | |
| I0404 15:59:13.356510 9252 solver.cpp:406] Test net output #1: loss/accuracy02 = 0.318 | |
| I0404 15:59:13.356523 9252 solver.cpp:406] Test net output #2: loss/accuracy03 = 0.283 | |
| I0404 15:59:13.356534 9252 solver.cpp:406] Test net output #3: loss/accuracy04 = 0.252 | |
| I0404 15:59:13.356546 9252 solver.cpp:406] Test net output #4: loss/accuracy05 = 0.326 | |
| I0404 15:59:13.356557 9252 solver.cpp:406] Test net output #5: loss/accuracy06 = 0.587 | |
| I0404 15:59:13.356569 9252 solver.cpp:406] Test net output #6: loss/accuracy07 = 0.894 | |
| I0404 15:59:13.356580 9252 solver.cpp:406] Test net output #7: loss/accuracy08 = 0.97 | |
| I0404 15:59:13.356591 9252 solver.cpp:406] Test net output #8: loss/accuracy09 = 0.995 | |
| I0404 15:59:13.356602 9252 solver.cpp:406] Test net output #9: loss/accuracy10 = 0.998 | |
| I0404 15:59:13.356613 9252 solver.cpp:406] Test net output #10: loss/accuracy11 = 1 | |
| I0404 15:59:13.356626 9252 solver.cpp:406] Test net output #11: loss/accuracy12 = 1 | |
| I0404 15:59:13.356637 9252 solver.cpp:406] Test net output #12: loss/accuracy13 = 1 | |
| I0404 15:59:13.356647 9252 solver.cpp:406] Test net output #13: loss/accuracy14 = 1 | |
| I0404 15:59:13.356658 9252 solver.cpp:406] Test net output #14: loss/accuracy15 = 1 | |
| I0404 15:59:13.356669 9252 solver.cpp:406] Test net output #15: loss/accuracy16 = 1 | |
| I0404 15:59:13.356680 9252 solver.cpp:406] Test net output #16: loss/accuracy17 = 1 | |
| I0404 15:59:13.356691 9252 solver.cpp:406] Test net output #17: loss/accuracy18 = 1 | |
| I0404 15:59:13.356703 9252 solver.cpp:406] Test net output #18: loss/accuracy19 = 1 | |
| I0404 15:59:13.356714 9252 solver.cpp:406] Test net output #19: loss/accuracy20 = 1 | |
| I0404 15:59:13.356724 9252 solver.cpp:406] Test net output #20: loss/accuracy21 = 1 | |
| I0404 15:59:13.356735 9252 solver.cpp:406] Test net output #21: loss/accuracy22 = 1 | |
| I0404 15:59:13.356753 9252 solver.cpp:406] Test net output #22: loss/loss01 = 1.8765 (* 0.0454545 = 0.0852957 loss) | |
| I0404 15:59:13.356768 9252 solver.cpp:406] Test net output #23: loss/loss02 = 2.44066 (* 0.0454545 = 0.110939 loss) | |
| I0404 15:59:13.356782 9252 solver.cpp:406] Test net output #24: loss/loss03 = 2.52585 (* 0.0454545 = 0.114812 loss) | |
| I0404 15:59:13.356796 9252 solver.cpp:406] Test net output #25: loss/loss04 = 2.65453 (* 0.0454545 = 0.120661 loss) | |
| I0404 15:59:13.356809 9252 solver.cpp:406] Test net output #26: loss/loss05 = 2.50783 (* 0.0454545 = 0.113992 loss) | |
| I0404 15:59:13.356822 9252 solver.cpp:406] Test net output #27: loss/loss06 = 1.6662 (* 0.0454545 = 0.0757364 loss) | |
| I0404 15:59:13.356835 9252 solver.cpp:406] Test net output #28: loss/loss07 = 0.500017 (* 0.0454545 = 0.022728 loss) | |
| I0404 15:59:13.356848 9252 solver.cpp:406] Test net output #29: loss/loss08 = 0.200395 (* 0.0454545 = 0.00910887 loss) | |
| I0404 15:59:13.356863 9252 solver.cpp:406] Test net output #30: loss/loss09 = 0.0527665 (* 0.0454545 = 0.00239848 loss) | |
| I0404 15:59:13.356875 9252 solver.cpp:406] Test net output #31: loss/loss10 = 0.0223847 (* 0.0454545 = 0.00101748 loss) | |
| I0404 15:59:13.356889 9252 solver.cpp:406] Test net output #32: loss/loss11 = 1.7538e-05 (* 0.0454545 = 7.97184e-07 loss) | |
| I0404 15:59:13.356902 9252 solver.cpp:406] Test net output #33: loss/loss12 = 1.49656e-05 (* 0.0454545 = 6.80253e-07 loss) | |
| I0404 15:59:13.356916 9252 solver.cpp:406] Test net output #34: loss/loss13 = 1.34282e-05 (* 0.0454545 = 6.10372e-07 loss) | |
| I0404 15:59:13.356930 9252 solver.cpp:406] Test net output #35: loss/loss14 = 1.46176e-05 (* 0.0454545 = 6.64436e-07 loss) | |
| I0404 15:59:13.356943 9252 solver.cpp:406] Test net output #36: loss/loss15 = 1.48311e-05 (* 0.0454545 = 6.7414e-07 loss) | |
| I0404 15:59:13.356957 9252 solver.cpp:406] Test net output #37: loss/loss16 = 1.53137e-05 (* 0.0454545 = 6.96077e-07 loss) | |
| I0404 15:59:13.356971 9252 solver.cpp:406] Test net output #38: loss/loss17 = 1.31764e-05 (* 0.0454545 = 5.98929e-07 loss) | |
| I0404 15:59:13.357017 9252 solver.cpp:406] Test net output #39: loss/loss18 = 1.49742e-05 (* 0.0454545 = 6.80644e-07 loss) | |
| I0404 15:59:13.357031 9252 solver.cpp:406] Test net output #40: loss/loss19 = 1.6242e-05 (* 0.0454545 = 7.38273e-07 loss) | |
| I0404 15:59:13.357045 9252 solver.cpp:406] Test net output #41: loss/loss20 = 1.58674e-05 (* 0.0454545 = 7.21247e-07 loss) | |
| I0404 15:59:13.357059 9252 solver.cpp:406] Test net output #42: loss/loss21 = 1.45515e-05 (* 0.0454545 = 6.61431e-07 loss) | |
| I0404 15:59:13.357072 9252 solver.cpp:406] Test net output #43: loss/loss22 = 1.31076e-05 (* 0.0454545 = 5.95801e-07 loss) | |
| I0404 15:59:13.357084 9252 solver.cpp:406] Test net output #44: total_accuracy = 0.006 | |
| I0404 15:59:13.357095 9252 solver.cpp:406] Test net output #45: total_confidence = 0.0105298 | |
| I0404 15:59:13.391345 9252 solver.cpp:229] Iteration 80000, loss = 0.707026 | |
| I0404 15:59:13.391381 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.40625 | |
| I0404 15:59:13.391398 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.3125 | |
| I0404 15:59:13.391410 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.125 | |
| I0404 15:59:13.391422 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.3125 | |
| I0404 15:59:13.391434 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.375 | |
| I0404 15:59:13.391446 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 15:59:13.391458 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.71875 | |
| I0404 15:59:13.391470 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 15:59:13.391482 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 15:59:13.391494 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 15:59:13.391506 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 15:59:13.391517 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 15:59:13.391532 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 15:59:13.391544 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 15:59:13.391556 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 15:59:13.391568 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 15:59:13.391579 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 15:59:13.391590 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 15:59:13.391602 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 15:59:13.391613 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 15:59:13.391624 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 15:59:13.391636 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 15:59:13.391650 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.12403 (* 0.0454545 = 0.0965467 loss) | |
| I0404 15:59:13.391664 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.09674 (* 0.0454545 = 0.0953064 loss) | |
| I0404 15:59:13.391679 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.88268 (* 0.0454545 = 0.131031 loss) | |
| I0404 15:59:13.391692 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.47025 (* 0.0454545 = 0.112284 loss) | |
| I0404 15:59:13.391705 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.42134 (* 0.0454545 = 0.110061 loss) | |
| I0404 15:59:13.391718 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.75323 (* 0.0454545 = 0.0796923 loss) | |
| I0404 15:59:13.391732 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.40424 (* 0.0454545 = 0.0638293 loss) | |
| I0404 15:59:13.391746 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.404233 (* 0.0454545 = 0.0183742 loss) | |
| I0404 15:59:13.391759 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.261289 (* 0.0454545 = 0.0118768 loss) | |
| I0404 15:59:13.391790 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.175902 (* 0.0454545 = 0.00799556 loss) | |
| I0404 15:59:13.391806 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.78725e-05 (* 0.0454545 = 8.12386e-07 loss) | |
| I0404 15:59:13.391820 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.49976e-05 (* 0.0454545 = 6.81709e-07 loss) | |
| I0404 15:59:13.391834 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.16143e-05 (* 0.0454545 = 5.27924e-07 loss) | |
| I0404 15:59:13.391849 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.61806e-05 (* 0.0454545 = 7.35482e-07 loss) | |
| I0404 15:59:13.391861 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.2844e-05 (* 0.0454545 = 5.8382e-07 loss) | |
| I0404 15:59:13.391875 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.55564e-05 (* 0.0454545 = 7.07108e-07 loss) | |
| I0404 15:59:13.391890 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.39134e-05 (* 0.0454545 = 6.32426e-07 loss) | |
| I0404 15:59:13.391906 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.43084e-05 (* 0.0454545 = 6.50381e-07 loss) | |
| I0404 15:59:13.391919 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.50983e-05 (* 0.0454545 = 6.86285e-07 loss) | |
| I0404 15:59:13.391933 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.46884e-05 (* 0.0454545 = 6.67655e-07 loss) | |
| I0404 15:59:13.391947 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.3332e-05 (* 0.0454545 = 6.06001e-07 loss) | |
| I0404 15:59:13.391962 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.13647e-05 (* 0.0454545 = 5.16578e-07 loss) | |
| I0404 15:59:13.391973 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 15:59:13.391984 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0148923 | |
| I0404 15:59:13.391999 9252 sgd_solver.cpp:106] Iteration 80000, lr = 0.0092 | |
| I0404 16:00:23.037235 9252 solver.cpp:229] Iteration 80500, loss = 0.698509 | |
| I0404 16:00:23.037400 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.40625 | |
| I0404 16:00:23.037422 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.375 | |
| I0404 16:00:23.037436 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.3125 | |
| I0404 16:00:23.037447 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.375 | |
| I0404 16:00:23.037459 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.40625 | |
| I0404 16:00:23.037472 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.40625 | |
| I0404 16:00:23.037483 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 16:00:23.037495 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 16:00:23.037508 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 16:00:23.037519 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 16:00:23.037531 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:00:23.037542 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:00:23.037554 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:00:23.037565 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:00:23.037577 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:00:23.037588 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:00:23.037600 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:00:23.037611 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:00:23.037623 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:00:23.037634 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:00:23.037645 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:00:23.037657 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:00:23.037672 9252 solver.cpp:245] Train net output #22: loss/loss01 = 2.00733 (* 0.0454545 = 0.0912421 loss) | |
| I0404 16:00:23.037686 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.33779 (* 0.0454545 = 0.106263 loss) | |
| I0404 16:00:23.037700 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.43276 (* 0.0454545 = 0.11058 loss) | |
| I0404 16:00:23.037714 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.33499 (* 0.0454545 = 0.106136 loss) | |
| I0404 16:00:23.037727 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.13121 (* 0.0454545 = 0.0968731 loss) | |
| I0404 16:00:23.037741 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.09274 (* 0.0454545 = 0.0951244 loss) | |
| I0404 16:00:23.037758 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.03788 (* 0.0454545 = 0.0471764 loss) | |
| I0404 16:00:23.037771 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.405575 (* 0.0454545 = 0.0184352 loss) | |
| I0404 16:00:23.037786 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.039235 (* 0.0454545 = 0.00178341 loss) | |
| I0404 16:00:23.037799 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0109014 (* 0.0454545 = 0.000495517 loss) | |
| I0404 16:00:23.037814 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.43152e-05 (* 0.0454545 = 6.50691e-07 loss) | |
| I0404 16:00:23.037828 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.40805e-05 (* 0.0454545 = 6.40025e-07 loss) | |
| I0404 16:00:23.037842 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.05169e-05 (* 0.0454545 = 4.78039e-07 loss) | |
| I0404 16:00:23.037855 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.37602e-05 (* 0.0454545 = 6.25464e-07 loss) | |
| I0404 16:00:23.037869 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.25084e-05 (* 0.0454545 = 5.68566e-07 loss) | |
| I0404 16:00:23.037883 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.03567e-05 (* 0.0454545 = 4.70761e-07 loss) | |
| I0404 16:00:23.037896 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.09678e-05 (* 0.0454545 = 4.98536e-07 loss) | |
| I0404 16:00:23.037925 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.21097e-05 (* 0.0454545 = 5.5044e-07 loss) | |
| I0404 16:00:23.037940 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.21582e-05 (* 0.0454545 = 5.52645e-07 loss) | |
| I0404 16:00:23.037955 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.10554e-05 (* 0.0454545 = 5.02517e-07 loss) | |
| I0404 16:00:23.037968 9252 solver.cpp:245] Train net output #42: loss/loss21 = 9.86496e-06 (* 0.0454545 = 4.48407e-07 loss) | |
| I0404 16:00:23.037982 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.02859e-05 (* 0.0454545 = 4.67542e-07 loss) | |
| I0404 16:00:23.037993 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 16:00:23.038005 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00389005 | |
| I0404 16:00:23.038018 9252 sgd_solver.cpp:106] Iteration 80500, lr = 0.009195 | |
| I0404 16:01:33.905815 9252 solver.cpp:229] Iteration 81000, loss = 0.693488 | |
| I0404 16:01:33.905918 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.46875 | |
| I0404 16:01:33.905937 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.46875 | |
| I0404 16:01:33.905949 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.25 | |
| I0404 16:01:33.905962 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.375 | |
| I0404 16:01:33.905973 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 16:01:33.905985 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 16:01:33.905997 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 16:01:33.906009 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 16:01:33.906020 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 16:01:33.906033 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 16:01:33.906044 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:01:33.906056 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:01:33.906067 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:01:33.906078 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:01:33.906090 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:01:33.906101 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:01:33.906113 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:01:33.906124 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:01:33.906136 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:01:33.906147 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:01:33.906158 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:01:33.906169 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:01:33.906184 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.97449 (* 0.0454545 = 0.0897495 loss) | |
| I0404 16:01:33.906198 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.05676 (* 0.0454545 = 0.093489 loss) | |
| I0404 16:01:33.906213 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.69528 (* 0.0454545 = 0.122513 loss) | |
| I0404 16:01:33.906226 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.4819 (* 0.0454545 = 0.112814 loss) | |
| I0404 16:01:33.906239 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.51299 (* 0.0454545 = 0.114227 loss) | |
| I0404 16:01:33.906252 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.69654 (* 0.0454545 = 0.0771157 loss) | |
| I0404 16:01:33.906266 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.70671 (* 0.0454545 = 0.0775776 loss) | |
| I0404 16:01:33.906280 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.573349 (* 0.0454545 = 0.0260613 loss) | |
| I0404 16:01:33.906293 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.225262 (* 0.0454545 = 0.0102392 loss) | |
| I0404 16:01:33.906308 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.0235437 (* 0.0454545 = 0.00107017 loss) | |
| I0404 16:01:33.906322 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.33768e-05 (* 0.0454545 = 6.08038e-07 loss) | |
| I0404 16:01:33.906337 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.07518e-05 (* 0.0454545 = 4.88719e-07 loss) | |
| I0404 16:01:33.906350 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.02322e-05 (* 0.0454545 = 4.651e-07 loss) | |
| I0404 16:01:33.906364 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.56572e-05 (* 0.0454545 = 7.11691e-07 loss) | |
| I0404 16:01:33.906378 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.05879e-05 (* 0.0454545 = 4.81268e-07 loss) | |
| I0404 16:01:33.906391 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.18976e-05 (* 0.0454545 = 5.40802e-07 loss) | |
| I0404 16:01:33.906405 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.0411e-05 (* 0.0454545 = 4.73229e-07 loss) | |
| I0404 16:01:33.906436 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.17262e-05 (* 0.0454545 = 5.33009e-07 loss) | |
| I0404 16:01:33.906451 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.07874e-05 (* 0.0454545 = 4.90337e-07 loss) | |
| I0404 16:01:33.906466 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.2382e-05 (* 0.0454545 = 5.62816e-07 loss) | |
| I0404 16:01:33.906482 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.18605e-05 (* 0.0454545 = 5.39113e-07 loss) | |
| I0404 16:01:33.906497 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.13462e-05 (* 0.0454545 = 5.15736e-07 loss) | |
| I0404 16:01:33.906509 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 16:01:33.906520 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0152995 | |
| I0404 16:01:33.906533 9252 sgd_solver.cpp:106] Iteration 81000, lr = 0.00919 | |
| I0404 16:02:46.041812 9252 solver.cpp:229] Iteration 81500, loss = 0.691573 | |
| I0404 16:02:46.042059 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.65625 | |
| I0404 16:02:46.042078 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.3125 | |
| I0404 16:02:46.042090 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.34375 | |
| I0404 16:02:46.042103 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.25 | |
| I0404 16:02:46.042114 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 16:02:46.042126 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5625 | |
| I0404 16:02:46.042137 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.90625 | |
| I0404 16:02:46.042150 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 16:02:46.042161 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 16:02:46.042173 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 16:02:46.042184 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:02:46.042196 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:02:46.042207 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:02:46.042218 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:02:46.042230 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:02:46.042242 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:02:46.042253 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:02:46.042264 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:02:46.042275 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:02:46.042286 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:02:46.042297 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:02:46.042309 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:02:46.042325 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.29568 (* 0.0454545 = 0.0588946 loss) | |
| I0404 16:02:46.042340 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.26837 (* 0.0454545 = 0.103108 loss) | |
| I0404 16:02:46.042354 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.28445 (* 0.0454545 = 0.103839 loss) | |
| I0404 16:02:46.042368 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.42106 (* 0.0454545 = 0.110048 loss) | |
| I0404 16:02:46.042382 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.42733 (* 0.0454545 = 0.110333 loss) | |
| I0404 16:02:46.042395 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.14525 (* 0.0454545 = 0.0975112 loss) | |
| I0404 16:02:46.042409 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.643101 (* 0.0454545 = 0.0292319 loss) | |
| I0404 16:02:46.042431 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.46215 (* 0.0454545 = 0.0210068 loss) | |
| I0404 16:02:46.042445 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.120168 (* 0.0454545 = 0.00546217 loss) | |
| I0404 16:02:46.042459 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.136831 (* 0.0454545 = 0.00621961 loss) | |
| I0404 16:02:46.042474 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.21054e-05 (* 0.0454545 = 5.50245e-07 loss) | |
| I0404 16:02:46.042489 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.67715e-05 (* 0.0454545 = 7.62341e-07 loss) | |
| I0404 16:02:46.042502 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.80442e-05 (* 0.0454545 = 8.20193e-07 loss) | |
| I0404 16:02:46.042515 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.68721e-05 (* 0.0454545 = 7.66912e-07 loss) | |
| I0404 16:02:46.042529 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.76678e-05 (* 0.0454545 = 8.0308e-07 loss) | |
| I0404 16:02:46.042543 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.5167e-05 (* 0.0454545 = 6.89408e-07 loss) | |
| I0404 16:02:46.042557 9252 solver.cpp:245] Train net output #38: loss/loss17 = 1.22209e-05 (* 0.0454545 = 5.55498e-07 loss) | |
| I0404 16:02:46.042587 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.35719e-05 (* 0.0454545 = 6.16903e-07 loss) | |
| I0404 16:02:46.042603 9252 solver.cpp:245] Train net output #40: loss/loss19 = 1.17738e-05 (* 0.0454545 = 5.35172e-07 loss) | |
| I0404 16:02:46.042618 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.31955e-05 (* 0.0454545 = 5.99794e-07 loss) | |
| I0404 16:02:46.042630 9252 solver.cpp:245] Train net output #42: loss/loss21 = 1.28825e-05 (* 0.0454545 = 5.8557e-07 loss) | |
| I0404 16:02:46.042644 9252 solver.cpp:245] Train net output #43: loss/loss22 = 1.62497e-05 (* 0.0454545 = 7.38622e-07 loss) | |
| I0404 16:02:46.042656 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 16:02:46.042667 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0101543 | |
| I0404 16:02:46.042680 9252 sgd_solver.cpp:106] Iteration 81500, lr = 0.009185 | |
| I0404 16:03:56.966033 9252 solver.cpp:229] Iteration 82000, loss = 0.690179 | |
| I0404 16:03:56.966311 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.5 | |
| I0404 16:03:56.966333 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.28125 | |
| I0404 16:03:56.966346 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.21875 | |
| I0404 16:03:56.966358 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.25 | |
| I0404 16:03:56.966378 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.34375 | |
| I0404 16:03:56.966390 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.53125 | |
| I0404 16:03:56.966418 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.8125 | |
| I0404 16:03:56.966452 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 16:03:56.966477 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 16:03:56.966495 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 16:03:56.966516 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:03:56.966528 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:03:56.966539 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:03:56.966555 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:03:56.966581 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:03:56.966593 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:03:56.966605 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:03:56.966616 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:03:56.966627 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:03:56.966639 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:03:56.966650 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:03:56.966665 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:03:56.966681 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.75419 (* 0.0454545 = 0.079736 loss) | |
| I0404 16:03:56.966696 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.31862 (* 0.0454545 = 0.105392 loss) | |
| I0404 16:03:56.966711 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.55595 (* 0.0454545 = 0.116179 loss) | |
| I0404 16:03:56.966732 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.39479 (* 0.0454545 = 0.108854 loss) | |
| I0404 16:03:56.966747 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.31656 (* 0.0454545 = 0.105298 loss) | |
| I0404 16:03:56.966759 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.80275 (* 0.0454545 = 0.081943 loss) | |
| I0404 16:03:56.966774 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.814896 (* 0.0454545 = 0.0370407 loss) | |
| I0404 16:03:56.966795 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.234584 (* 0.0454545 = 0.0106629 loss) | |
| I0404 16:03:56.966809 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.2484 (* 0.0454545 = 0.0112909 loss) | |
| I0404 16:03:56.966823 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.137102 (* 0.0454545 = 0.00623192 loss) | |
| I0404 16:03:56.966837 9252 solver.cpp:245] Train net output #32: loss/loss11 = 9.63784e-06 (* 0.0454545 = 4.38084e-07 loss) | |
| I0404 16:03:56.966852 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.14804e-05 (* 0.0454545 = 5.21834e-07 loss) | |
| I0404 16:03:56.966866 9252 solver.cpp:245] Train net output #34: loss/loss13 = 9.62296e-06 (* 0.0454545 = 4.37407e-07 loss) | |
| I0404 16:03:56.966881 9252 solver.cpp:245] Train net output #35: loss/loss14 = 8.84436e-06 (* 0.0454545 = 4.02016e-07 loss) | |
| I0404 16:03:56.966894 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.00179e-05 (* 0.0454545 = 4.55359e-07 loss) | |
| I0404 16:03:56.966908 9252 solver.cpp:245] Train net output #37: loss/loss16 = 9.71437e-06 (* 0.0454545 = 4.41562e-07 loss) | |
| I0404 16:03:56.966922 9252 solver.cpp:245] Train net output #38: loss/loss17 = 7.8272e-06 (* 0.0454545 = 3.55782e-07 loss) | |
| I0404 16:03:56.966954 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.66699e-06 (* 0.0454545 = 3.485e-07 loss) | |
| I0404 16:03:56.966970 9252 solver.cpp:245] Train net output #40: loss/loss19 = 7.69683e-06 (* 0.0454545 = 3.49856e-07 loss) | |
| I0404 16:03:56.966989 9252 solver.cpp:245] Train net output #41: loss/loss20 = 7.94646e-06 (* 0.0454545 = 3.61203e-07 loss) | |
| I0404 16:03:56.967003 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.88313e-06 (* 0.0454545 = 3.58324e-07 loss) | |
| I0404 16:03:56.967017 9252 solver.cpp:245] Train net output #43: loss/loss22 = 9.3287e-06 (* 0.0454545 = 4.24032e-07 loss) | |
| I0404 16:03:56.967030 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 16:03:56.967042 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00341853 | |
| I0404 16:03:56.967058 9252 sgd_solver.cpp:106] Iteration 82000, lr = 0.00918 | |
| I0404 16:05:08.190829 9252 solver.cpp:229] Iteration 82500, loss = 0.687545 | |
| I0404 16:05:08.190975 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.625 | |
| I0404 16:05:08.190997 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.28125 | |
| I0404 16:05:08.191009 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.28125 | |
| I0404 16:05:08.191021 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.375 | |
| I0404 16:05:08.191033 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.40625 | |
| I0404 16:05:08.191046 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 16:05:08.191058 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.6875 | |
| I0404 16:05:08.191071 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.8125 | |
| I0404 16:05:08.191082 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.90625 | |
| I0404 16:05:08.191093 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.9375 | |
| I0404 16:05:08.191105 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:05:08.191118 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:05:08.191128 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:05:08.191140 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:05:08.191153 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:05:08.191169 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:05:08.191193 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:05:08.191216 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:05:08.191231 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:05:08.191242 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:05:08.191254 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:05:08.191267 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:05:08.191282 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.39683 (* 0.0454545 = 0.0634921 loss) | |
| I0404 16:05:08.191298 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.43534 (* 0.0454545 = 0.110697 loss) | |
| I0404 16:05:08.191311 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.22946 (* 0.0454545 = 0.101339 loss) | |
| I0404 16:05:08.191325 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.29561 (* 0.0454545 = 0.104346 loss) | |
| I0404 16:05:08.191339 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.25728 (* 0.0454545 = 0.102604 loss) | |
| I0404 16:05:08.191352 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.9378 (* 0.0454545 = 0.0880816 loss) | |
| I0404 16:05:08.191366 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.27531 (* 0.0454545 = 0.0579689 loss) | |
| I0404 16:05:08.191380 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.758353 (* 0.0454545 = 0.0344706 loss) | |
| I0404 16:05:08.191393 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.365166 (* 0.0454545 = 0.0165985 loss) | |
| I0404 16:05:08.191407 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.193861 (* 0.0454545 = 0.00881186 loss) | |
| I0404 16:05:08.191422 9252 solver.cpp:245] Train net output #32: loss/loss11 = 2.72256e-05 (* 0.0454545 = 1.23753e-06 loss) | |
| I0404 16:05:08.191437 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.09137e-05 (* 0.0454545 = 9.50621e-07 loss) | |
| I0404 16:05:08.191455 9252 solver.cpp:245] Train net output #34: loss/loss13 = 2.70242e-05 (* 0.0454545 = 1.22837e-06 loss) | |
| I0404 16:05:08.191486 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.7649e-05 (* 0.0454545 = 1.25677e-06 loss) | |
| I0404 16:05:08.191511 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.09098e-05 (* 0.0454545 = 9.50444e-07 loss) | |
| I0404 16:05:08.191526 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.58785e-05 (* 0.0454545 = 1.1763e-06 loss) | |
| I0404 16:05:08.191541 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.58954e-05 (* 0.0454545 = 1.17707e-06 loss) | |
| I0404 16:05:08.191570 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.17071e-05 (* 0.0454545 = 9.86686e-07 loss) | |
| I0404 16:05:08.191586 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.41699e-05 (* 0.0454545 = 1.09863e-06 loss) | |
| I0404 16:05:08.191599 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.37697e-05 (* 0.0454545 = 1.08044e-06 loss) | |
| I0404 16:05:08.191612 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.23817e-05 (* 0.0454545 = 1.01735e-06 loss) | |
| I0404 16:05:08.191630 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.30951e-05 (* 0.0454545 = 1.04978e-06 loss) | |
| I0404 16:05:08.191642 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 16:05:08.191654 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0121343 | |
| I0404 16:05:08.191669 9252 sgd_solver.cpp:106] Iteration 82500, lr = 0.009175 | |
| I0404 16:06:19.174793 9252 solver.cpp:229] Iteration 83000, loss = 0.684454 | |
| I0404 16:06:19.174942 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.59375 | |
| I0404 16:06:19.174962 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.375 | |
| I0404 16:06:19.174974 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.15625 | |
| I0404 16:06:19.174986 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.28125 | |
| I0404 16:06:19.174998 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.40625 | |
| I0404 16:06:19.175010 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.5 | |
| I0404 16:06:19.175021 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 16:06:19.175034 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.9375 | |
| I0404 16:06:19.175045 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 16:06:19.175057 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 16:06:19.175068 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:06:19.175081 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:06:19.175092 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:06:19.175103 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:06:19.175115 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:06:19.175127 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:06:19.175137 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:06:19.175148 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:06:19.175159 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:06:19.175171 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:06:19.175182 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:06:19.175194 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:06:19.175209 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.6548 (* 0.0454545 = 0.0752183 loss) | |
| I0404 16:06:19.175225 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.40046 (* 0.0454545 = 0.109112 loss) | |
| I0404 16:06:19.175238 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.60376 (* 0.0454545 = 0.118353 loss) | |
| I0404 16:06:19.175251 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.50523 (* 0.0454545 = 0.113874 loss) | |
| I0404 16:06:19.175266 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.19162 (* 0.0454545 = 0.099619 loss) | |
| I0404 16:06:19.175278 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.81274 (* 0.0454545 = 0.0823974 loss) | |
| I0404 16:06:19.175300 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.72795 (* 0.0454545 = 0.0330887 loss) | |
| I0404 16:06:19.175314 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.260282 (* 0.0454545 = 0.011831 loss) | |
| I0404 16:06:19.175328 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.361783 (* 0.0454545 = 0.0164447 loss) | |
| I0404 16:06:19.175341 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.133137 (* 0.0454545 = 0.00605169 loss) | |
| I0404 16:06:19.175362 9252 solver.cpp:245] Train net output #32: loss/loss11 = 3.03372e-05 (* 0.0454545 = 1.37896e-06 loss) | |
| I0404 16:06:19.175376 9252 solver.cpp:245] Train net output #33: loss/loss12 = 2.45802e-05 (* 0.0454545 = 1.11728e-06 loss) | |
| I0404 16:06:19.175390 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.99638e-05 (* 0.0454545 = 9.07447e-07 loss) | |
| I0404 16:06:19.175405 9252 solver.cpp:245] Train net output #35: loss/loss14 = 2.32215e-05 (* 0.0454545 = 1.05552e-06 loss) | |
| I0404 16:06:19.175418 9252 solver.cpp:245] Train net output #36: loss/loss15 = 2.43807e-05 (* 0.0454545 = 1.10821e-06 loss) | |
| I0404 16:06:19.175432 9252 solver.cpp:245] Train net output #37: loss/loss16 = 2.67961e-05 (* 0.0454545 = 1.21801e-06 loss) | |
| I0404 16:06:19.175446 9252 solver.cpp:245] Train net output #38: loss/loss17 = 2.15178e-05 (* 0.0454545 = 9.78082e-07 loss) | |
| I0404 16:06:19.175477 9252 solver.cpp:245] Train net output #39: loss/loss18 = 2.27611e-05 (* 0.0454545 = 1.03459e-06 loss) | |
| I0404 16:06:19.175493 9252 solver.cpp:245] Train net output #40: loss/loss19 = 2.461e-05 (* 0.0454545 = 1.11864e-06 loss) | |
| I0404 16:06:19.175506 9252 solver.cpp:245] Train net output #41: loss/loss20 = 2.56112e-05 (* 0.0454545 = 1.16415e-06 loss) | |
| I0404 16:06:19.175520 9252 solver.cpp:245] Train net output #42: loss/loss21 = 2.16878e-05 (* 0.0454545 = 9.85811e-07 loss) | |
| I0404 16:06:19.175534 9252 solver.cpp:245] Train net output #43: loss/loss22 = 2.29906e-05 (* 0.0454545 = 1.04503e-06 loss) | |
| I0404 16:06:19.175545 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 16:06:19.175557 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00637018 | |
| I0404 16:06:19.175571 9252 sgd_solver.cpp:106] Iteration 83000, lr = 0.00917 | |
| I0404 16:07:30.606639 9252 solver.cpp:229] Iteration 83500, loss = 0.68729 | |
| I0404 16:07:30.606768 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.71875 | |
| I0404 16:07:30.606788 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.3125 | |
| I0404 16:07:30.606801 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.40625 | |
| I0404 16:07:30.606813 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.21875 | |
| I0404 16:07:30.606825 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.5 | |
| I0404 16:07:30.606837 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.4375 | |
| I0404 16:07:30.606848 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.625 | |
| I0404 16:07:30.606860 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 16:07:30.606871 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 16:07:30.606884 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 16:07:30.606894 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:07:30.606906 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:07:30.606917 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:07:30.606928 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:07:30.606940 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:07:30.606950 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:07:30.606962 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:07:30.606973 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:07:30.606986 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:07:30.606997 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:07:30.607008 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:07:30.607019 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:07:30.607035 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.15319 (* 0.0454545 = 0.0524179 loss) | |
| I0404 16:07:30.607049 9252 solver.cpp:245] Train net output #23: loss/loss02 = 1.99735 (* 0.0454545 = 0.0907888 loss) | |
| I0404 16:07:30.607064 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.14989 (* 0.0454545 = 0.0977222 loss) | |
| I0404 16:07:30.607077 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.70075 (* 0.0454545 = 0.122761 loss) | |
| I0404 16:07:30.607090 9252 solver.cpp:245] Train net output #26: loss/loss05 = 1.97597 (* 0.0454545 = 0.0898168 loss) | |
| I0404 16:07:30.607105 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.01968 (* 0.0454545 = 0.0918039 loss) | |
| I0404 16:07:30.607117 9252 solver.cpp:245] Train net output #28: loss/loss07 = 1.1437 (* 0.0454545 = 0.0519865 loss) | |
| I0404 16:07:30.607131 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.451574 (* 0.0454545 = 0.0205261 loss) | |
| I0404 16:07:30.607144 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.318654 (* 0.0454545 = 0.0144843 loss) | |
| I0404 16:07:30.607158 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.163731 (* 0.0454545 = 0.00744232 loss) | |
| I0404 16:07:30.607172 9252 solver.cpp:245] Train net output #32: loss/loss11 = 8.04315e-06 (* 0.0454545 = 3.65598e-07 loss) | |
| I0404 16:07:30.607187 9252 solver.cpp:245] Train net output #33: loss/loss12 = 7.693e-06 (* 0.0454545 = 3.49682e-07 loss) | |
| I0404 16:07:30.607200 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.40178e-06 (* 0.0454545 = 2.45535e-07 loss) | |
| I0404 16:07:30.607213 9252 solver.cpp:245] Train net output #35: loss/loss14 = 6.892e-06 (* 0.0454545 = 3.13273e-07 loss) | |
| I0404 16:07:30.607228 9252 solver.cpp:245] Train net output #36: loss/loss15 = 6.97765e-06 (* 0.0454545 = 3.17166e-07 loss) | |
| I0404 16:07:30.607241 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.75418e-06 (* 0.0454545 = 3.07008e-07 loss) | |
| I0404 16:07:30.607255 9252 solver.cpp:245] Train net output #38: loss/loss17 = 5.40551e-06 (* 0.0454545 = 2.45705e-07 loss) | |
| I0404 16:07:30.607286 9252 solver.cpp:245] Train net output #39: loss/loss18 = 7.43591e-06 (* 0.0454545 = 3.37996e-07 loss) | |
| I0404 16:07:30.607301 9252 solver.cpp:245] Train net output #40: loss/loss19 = 5.67377e-06 (* 0.0454545 = 2.57899e-07 loss) | |
| I0404 16:07:30.607316 9252 solver.cpp:245] Train net output #41: loss/loss20 = 6.19906e-06 (* 0.0454545 = 2.81775e-07 loss) | |
| I0404 16:07:30.607329 9252 solver.cpp:245] Train net output #42: loss/loss21 = 6.4077e-06 (* 0.0454545 = 2.91259e-07 loss) | |
| I0404 16:07:30.607342 9252 solver.cpp:245] Train net output #43: loss/loss22 = 5.64395e-06 (* 0.0454545 = 2.56543e-07 loss) | |
| I0404 16:07:30.607354 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 16:07:30.607365 9252 solver.cpp:245] Train net output #45: total_confidence = 0.00870726 | |
| I0404 16:07:30.607379 9252 sgd_solver.cpp:106] Iteration 83500, lr = 0.009165 | |
| I0404 16:08:41.702541 9252 solver.cpp:229] Iteration 84000, loss = 0.678083 | |
| I0404 16:08:41.702657 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.59375 | |
| I0404 16:08:41.702677 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.4375 | |
| I0404 16:08:41.702689 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.4375 | |
| I0404 16:08:41.702702 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.1875 | |
| I0404 16:08:41.702713 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.53125 | |
| I0404 16:08:41.702725 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 16:08:41.702738 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.78125 | |
| I0404 16:08:41.702751 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 1 | |
| I0404 16:08:41.702764 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 1 | |
| I0404 16:08:41.702775 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 16:08:41.702787 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:08:41.702798 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:08:41.702811 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:08:41.702821 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:08:41.702833 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:08:41.702844 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:08:41.702857 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:08:41.702867 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:08:41.702879 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:08:41.702890 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:08:41.702903 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:08:41.702913 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:08:41.702929 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.30998 (* 0.0454545 = 0.0595444 loss) | |
| I0404 16:08:41.702944 9252 solver.cpp:245] Train net output #23: loss/loss02 = 1.84673 (* 0.0454545 = 0.0839422 loss) | |
| I0404 16:08:41.702960 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.11129 (* 0.0454545 = 0.0959677 loss) | |
| I0404 16:08:41.702972 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.79961 (* 0.0454545 = 0.127255 loss) | |
| I0404 16:08:41.702986 9252 solver.cpp:245] Train net output #26: loss/loss05 = 1.85139 (* 0.0454545 = 0.0841541 loss) | |
| I0404 16:08:41.702999 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.85572 (* 0.0454545 = 0.0843508 loss) | |
| I0404 16:08:41.703012 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.847518 (* 0.0454545 = 0.0385235 loss) | |
| I0404 16:08:41.703027 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.0951699 (* 0.0454545 = 0.0043259 loss) | |
| I0404 16:08:41.703040 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.028569 (* 0.0454545 = 0.00129859 loss) | |
| I0404 16:08:41.703054 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00931358 (* 0.0454545 = 0.000423344 loss) | |
| I0404 16:08:41.703068 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.3552e-05 (* 0.0454545 = 6.15999e-07 loss) | |
| I0404 16:08:41.703083 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.19871e-05 (* 0.0454545 = 5.44868e-07 loss) | |
| I0404 16:08:41.703096 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.35373e-05 (* 0.0454545 = 6.1533e-07 loss) | |
| I0404 16:08:41.703110 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.28369e-05 (* 0.0454545 = 5.83494e-07 loss) | |
| I0404 16:08:41.703124 9252 solver.cpp:245] Train net output #36: loss/loss15 = 1.14954e-05 (* 0.0454545 = 5.22518e-07 loss) | |
| I0404 16:08:41.703137 9252 solver.cpp:245] Train net output #37: loss/loss16 = 6.85113e-06 (* 0.0454545 = 3.11415e-07 loss) | |
| I0404 16:08:41.703151 9252 solver.cpp:245] Train net output #38: loss/loss17 = 9.4424e-06 (* 0.0454545 = 4.292e-07 loss) | |
| I0404 16:08:41.703182 9252 solver.cpp:245] Train net output #39: loss/loss18 = 1.05155e-05 (* 0.0454545 = 4.77978e-07 loss) | |
| I0404 16:08:41.703198 9252 solver.cpp:245] Train net output #40: loss/loss19 = 6.94799e-06 (* 0.0454545 = 3.15818e-07 loss) | |
| I0404 16:08:41.703212 9252 solver.cpp:245] Train net output #41: loss/loss20 = 1.11193e-05 (* 0.0454545 = 5.05423e-07 loss) | |
| I0404 16:08:41.703225 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.57394e-06 (* 0.0454545 = 3.4427e-07 loss) | |
| I0404 16:08:41.703239 9252 solver.cpp:245] Train net output #43: loss/loss22 = 7.36528e-06 (* 0.0454545 = 3.34786e-07 loss) | |
| I0404 16:08:41.703251 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 16:08:41.703263 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0240105 | |
| I0404 16:08:41.703275 9252 sgd_solver.cpp:106] Iteration 84000, lr = 0.00916 | |
| I0404 16:09:53.247364 9252 solver.cpp:229] Iteration 84500, loss = 0.682726 | |
| I0404 16:09:53.247527 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.5 | |
| I0404 16:09:53.247547 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.28125 | |
| I0404 16:09:53.247560 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.46875 | |
| I0404 16:09:53.247572 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.3125 | |
| I0404 16:09:53.247584 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.28125 | |
| I0404 16:09:53.247596 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.53125 | |
| I0404 16:09:53.247608 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.875 | |
| I0404 16:09:53.247620 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.96875 | |
| I0404 16:09:53.247632 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 16:09:53.247644 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 1 | |
| I0404 16:09:53.247656 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:09:53.247668 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:09:53.247679 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:09:53.247690 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:09:53.247702 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:09:53.247714 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:09:53.247725 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:09:53.247736 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:09:53.247750 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:09:53.247762 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:09:53.247773 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:09:53.247786 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:09:53.247800 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.66958 (* 0.0454545 = 0.0758902 loss) | |
| I0404 16:09:53.247815 9252 solver.cpp:245] Train net output #23: loss/loss02 = 1.98509 (* 0.0454545 = 0.0902315 loss) | |
| I0404 16:09:53.247829 9252 solver.cpp:245] Train net output #24: loss/loss03 = 1.98886 (* 0.0454545 = 0.0904029 loss) | |
| I0404 16:09:53.247843 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.70554 (* 0.0454545 = 0.122979 loss) | |
| I0404 16:09:53.247858 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.36568 (* 0.0454545 = 0.107531 loss) | |
| I0404 16:09:53.247871 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.86302 (* 0.0454545 = 0.0846827 loss) | |
| I0404 16:09:53.247884 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.667395 (* 0.0454545 = 0.0303361 loss) | |
| I0404 16:09:53.247898 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.16812 (* 0.0454545 = 0.0076418 loss) | |
| I0404 16:09:53.247912 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.163225 (* 0.0454545 = 0.00741932 loss) | |
| I0404 16:09:53.247926 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.00339581 (* 0.0454545 = 0.000154355 loss) | |
| I0404 16:09:53.247941 9252 solver.cpp:245] Train net output #32: loss/loss11 = 1.46405e-06 (* 0.0454545 = 6.65479e-08 loss) | |
| I0404 16:09:53.247954 9252 solver.cpp:245] Train net output #33: loss/loss12 = 1.02446e-06 (* 0.0454545 = 4.65664e-08 loss) | |
| I0404 16:09:53.247968 9252 solver.cpp:245] Train net output #34: loss/loss13 = 1.24426e-06 (* 0.0454545 = 5.65571e-08 loss) | |
| I0404 16:09:53.247982 9252 solver.cpp:245] Train net output #35: loss/loss14 = 1.14367e-06 (* 0.0454545 = 5.1985e-08 loss) | |
| I0404 16:09:53.247997 9252 solver.cpp:245] Train net output #36: loss/loss15 = 9.76031e-07 (* 0.0454545 = 4.43651e-08 loss) | |
| I0404 16:09:53.248009 9252 solver.cpp:245] Train net output #37: loss/loss16 = 1.22936e-06 (* 0.0454545 = 5.58798e-08 loss) | |
| I0404 16:09:53.248023 9252 solver.cpp:245] Train net output #38: loss/loss17 = 9.23876e-07 (* 0.0454545 = 4.19944e-08 loss) | |
| I0404 16:09:53.248051 9252 solver.cpp:245] Train net output #39: loss/loss18 = 8.12116e-07 (* 0.0454545 = 3.69144e-08 loss) | |
| I0404 16:09:53.248066 9252 solver.cpp:245] Train net output #40: loss/loss19 = 8.86623e-07 (* 0.0454545 = 4.0301e-08 loss) | |
| I0404 16:09:53.248080 9252 solver.cpp:245] Train net output #41: loss/loss20 = 9.35052e-07 (* 0.0454545 = 4.25024e-08 loss) | |
| I0404 16:09:53.248093 9252 solver.cpp:245] Train net output #42: loss/loss21 = 7.63687e-07 (* 0.0454545 = 3.47131e-08 loss) | |
| I0404 16:09:53.248107 9252 solver.cpp:245] Train net output #43: loss/loss22 = 9.87208e-07 (* 0.0454545 = 4.48731e-08 loss) | |
| I0404 16:09:53.248119 9252 solver.cpp:245] Train net output #44: total_accuracy = 0.03125 | |
| I0404 16:09:53.248131 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0115359 | |
| I0404 16:09:53.248144 9252 sgd_solver.cpp:106] Iteration 84500, lr = 0.009155 | |
| I0404 16:11:04.140226 9252 solver.cpp:229] Iteration 85000, loss = 0.671853 | |
| I0404 16:11:04.140362 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.59375 | |
| I0404 16:11:04.140380 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.40625 | |
| I0404 16:11:04.140393 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.25 | |
| I0404 16:11:04.140405 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.28125 | |
| I0404 16:11:04.140418 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.375 | |
| I0404 16:11:04.140429 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.46875 | |
| I0404 16:11:04.140440 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.75 | |
| I0404 16:11:04.140452 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.90625 | |
| I0404 16:11:04.140465 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.9375 | |
| I0404 16:11:04.140475 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 16:11:04.140487 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:11:04.140499 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:11:04.140511 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:11:04.140522 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:11:04.140534 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:11:04.140545 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:11:04.140557 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:11:04.140568 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:11:04.140579 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:11:04.140590 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:11:04.140601 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:11:04.140612 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:11:04.140629 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.12883 (* 0.0454545 = 0.0513106 loss) | |
| I0404 16:11:04.140642 9252 solver.cpp:245] Train net output #23: loss/loss02 = 2.17018 (* 0.0454545 = 0.0986447 loss) | |
| I0404 16:11:04.140656 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.57401 (* 0.0454545 = 0.117 loss) | |
| I0404 16:11:04.140671 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.37569 (* 0.0454545 = 0.107986 loss) | |
| I0404 16:11:04.140684 9252 solver.cpp:245] Train net output #26: loss/loss05 = 2.56213 (* 0.0454545 = 0.116461 loss) | |
| I0404 16:11:04.140698 9252 solver.cpp:245] Train net output #27: loss/loss06 = 2.02572 (* 0.0454545 = 0.0920781 loss) | |
| I0404 16:11:04.140712 9252 solver.cpp:245] Train net output #28: loss/loss07 = 0.922863 (* 0.0454545 = 0.0419483 loss) | |
| I0404 16:11:04.140725 9252 solver.cpp:245] Train net output #29: loss/loss08 = 0.401604 (* 0.0454545 = 0.0182547 loss) | |
| I0404 16:11:04.140738 9252 solver.cpp:245] Train net output #30: loss/loss09 = 0.283068 (* 0.0454545 = 0.0128667 loss) | |
| I0404 16:11:04.140755 9252 solver.cpp:245] Train net output #31: loss/loss10 = 0.220708 (* 0.0454545 = 0.0100322 loss) | |
| I0404 16:11:04.140770 9252 solver.cpp:245] Train net output #32: loss/loss11 = 6.75045e-06 (* 0.0454545 = 3.06838e-07 loss) | |
| I0404 16:11:04.140784 9252 solver.cpp:245] Train net output #33: loss/loss12 = 5.81532e-06 (* 0.0454545 = 2.64333e-07 loss) | |
| I0404 16:11:04.140799 9252 solver.cpp:245] Train net output #34: loss/loss13 = 5.69987e-06 (* 0.0454545 = 2.59085e-07 loss) | |
| I0404 16:11:04.140811 9252 solver.cpp:245] Train net output #35: loss/loss14 = 5.90849e-06 (* 0.0454545 = 2.68568e-07 loss) | |
| I0404 16:11:04.140825 9252 solver.cpp:245] Train net output #36: loss/loss15 = 4.74985e-06 (* 0.0454545 = 2.15902e-07 loss) | |
| I0404 16:11:04.140839 9252 solver.cpp:245] Train net output #37: loss/loss16 = 5.38691e-06 (* 0.0454545 = 2.44859e-07 loss) | |
| I0404 16:11:04.140852 9252 solver.cpp:245] Train net output #38: loss/loss17 = 4.99203e-06 (* 0.0454545 = 2.26911e-07 loss) | |
| I0404 16:11:04.140884 9252 solver.cpp:245] Train net output #39: loss/loss18 = 5.11122e-06 (* 0.0454545 = 2.32328e-07 loss) | |
| I0404 16:11:04.140899 9252 solver.cpp:245] Train net output #40: loss/loss19 = 4.57476e-06 (* 0.0454545 = 2.07944e-07 loss) | |
| I0404 16:11:04.140914 9252 solver.cpp:245] Train net output #41: loss/loss20 = 5.3236e-06 (* 0.0454545 = 2.41982e-07 loss) | |
| I0404 16:11:04.140928 9252 solver.cpp:245] Train net output #42: loss/loss21 = 4.42574e-06 (* 0.0454545 = 2.0117e-07 loss) | |
| I0404 16:11:04.140943 9252 solver.cpp:245] Train net output #43: loss/loss22 = 4.82438e-06 (* 0.0454545 = 2.1929e-07 loss) | |
| I0404 16:11:04.140954 9252 solver.cpp:245] Train net output #44: total_accuracy = 0 | |
| I0404 16:11:04.140965 9252 solver.cpp:245] Train net output #45: total_confidence = 0.0098299 | |
| I0404 16:11:04.140981 9252 sgd_solver.cpp:106] Iteration 85000, lr = 0.00915 | |
| I0404 16:12:15.295441 9252 solver.cpp:229] Iteration 85500, loss = 0.672208 | |
| I0404 16:12:15.295584 9252 solver.cpp:245] Train net output #0: loss/accuracy01 = 0.59375 | |
| I0404 16:12:15.295604 9252 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.5 | |
| I0404 16:12:15.295616 9252 solver.cpp:245] Train net output #2: loss/accuracy03 = 0.25 | |
| I0404 16:12:15.295629 9252 solver.cpp:245] Train net output #3: loss/accuracy04 = 0.375 | |
| I0404 16:12:15.295640 9252 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.59375 | |
| I0404 16:12:15.295652 9252 solver.cpp:245] Train net output #5: loss/accuracy06 = 0.6875 | |
| I0404 16:12:15.295663 9252 solver.cpp:245] Train net output #6: loss/accuracy07 = 0.84375 | |
| I0404 16:12:15.295675 9252 solver.cpp:245] Train net output #7: loss/accuracy08 = 0.875 | |
| I0404 16:12:15.295687 9252 solver.cpp:245] Train net output #8: loss/accuracy09 = 0.96875 | |
| I0404 16:12:15.295698 9252 solver.cpp:245] Train net output #9: loss/accuracy10 = 0.96875 | |
| I0404 16:12:15.295711 9252 solver.cpp:245] Train net output #10: loss/accuracy11 = 1 | |
| I0404 16:12:15.295722 9252 solver.cpp:245] Train net output #11: loss/accuracy12 = 1 | |
| I0404 16:12:15.295733 9252 solver.cpp:245] Train net output #12: loss/accuracy13 = 1 | |
| I0404 16:12:15.295747 9252 solver.cpp:245] Train net output #13: loss/accuracy14 = 1 | |
| I0404 16:12:15.295759 9252 solver.cpp:245] Train net output #14: loss/accuracy15 = 1 | |
| I0404 16:12:15.295770 9252 solver.cpp:245] Train net output #15: loss/accuracy16 = 1 | |
| I0404 16:12:15.295783 9252 solver.cpp:245] Train net output #16: loss/accuracy17 = 1 | |
| I0404 16:12:15.295794 9252 solver.cpp:245] Train net output #17: loss/accuracy18 = 1 | |
| I0404 16:12:15.295805 9252 solver.cpp:245] Train net output #18: loss/accuracy19 = 1 | |
| I0404 16:12:15.295816 9252 solver.cpp:245] Train net output #19: loss/accuracy20 = 1 | |
| I0404 16:12:15.295828 9252 solver.cpp:245] Train net output #20: loss/accuracy21 = 1 | |
| I0404 16:12:15.295840 9252 solver.cpp:245] Train net output #21: loss/accuracy22 = 1 | |
| I0404 16:12:15.295855 9252 solver.cpp:245] Train net output #22: loss/loss01 = 1.33762 (* 0.0454545 = 0.060801 loss) | |
| I0404 16:12:15.295871 9252 solver.cpp:245] Train net output #23: loss/loss02 = 1.74009 (* 0.0454545 = 0.0790949 loss) | |
| I0404 16:12:15.295883 9252 solver.cpp:245] Train net output #24: loss/loss03 = 2.28857 (* 0.0454545 = 0.104026 loss) | |
| I0404 16:12:15.295897 9252 solver.cpp:245] Train net output #25: loss/loss04 = 2.16269 (* 0.0454545 = 0.0983043 loss) | |
| I0404 16:12:15.295910 9252 solver.cpp:245] Train net output #26: loss/loss05 = 1.67149 (* 0.0454545 = 0.0759768 loss) | |
| I0404 16:12:15.295924 9252 solver.cpp:245] Train net output #27: loss/loss06 = 1.46345 (* 0.0454545 = 0.0665206 loss) | |
| I0404 16:12:15.29593 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment