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| I0401 10:07:22.297281 31447 solver.cpp:280] Solving mixed_lstm | |
| I0401 10:07:22.297292 31447 solver.cpp:281] Learning Rate Policy: fixed | |
| I0401 10:07:22.643712 31447 solver.cpp:229] Iteration 0, loss = 14.0021 | |
| I0401 10:07:22.643754 31447 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0401 10:07:22.643769 31447 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0 | |
| I0401 10:07:22.643782 31447 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.0238095 | |
| I0401 10:07:22.643798 31447 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 4.45928 (* 0.3 = 1.33778 loss) | |
| I0401 10:07:22.643812 31447 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 4.36399 (* 0.3 = 1.3092 loss) | |
| I0401 10:07:22.643826 31447 solver.cpp:245] Train net output #5: loss2/accuracy = 0 | |
| I0401 10:07:22.643837 31447 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0 |
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| I0401 12:44:49.957870 6134 solver.cpp:280] Solving mixed_lstm | |
| I0401 12:44:49.957882 6134 solver.cpp:281] Learning Rate Policy: fixed | |
| I0401 12:44:50.306246 6134 solver.cpp:229] Iteration 0, loss = 13.7773 | |
| I0401 12:44:50.306291 6134 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0401 12:44:50.306309 6134 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.181818 | |
| I0401 12:44:50.306321 6134 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.0222222 | |
| I0401 12:44:50.306339 6134 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 4.39462 (* 0.3 = 1.31839 loss) | |
| I0401 12:44:50.306352 6134 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 4.19871 (* 0.3 = 1.25961 loss) | |
| I0401 12:44:50.306365 6134 solver.cpp:245] Train net output #5: loss2/accuracy = 0 | |
| I0401 12:44:50.306397 6134 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0 |
stas-sl
/ firenet150x50_len22_2_trainval
Last active
April 4, 2016 17:21
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| 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 |
stas-sl
/ firenet150x50_len22_3_trainval
Last active
April 5, 2016 10:45
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| I0404 23:42:02.924360 26022 solver.cpp:280] Solving | |
| I0404 23:42:02.924372 26022 solver.cpp:281] Learning Rate Policy: poly | |
| I0404 23:42:13.433935 26022 solver.cpp:229] Iteration 0, loss = 4.304 | |
| I0404 23:42:13.433985 26022 solver.cpp:245] Train net output #0: loss/accuracy01 = 0 | |
| I0404 23:42:13.434006 26022 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0404 23:42:13.434020 26022 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0404 23:42:13.434031 26022 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0404 23:42:13.434043 26022 solver.cpp:245] Train net output #4: loss/accuracy05 = 0 | |
| I0404 23:42:13.434056 26022 solver.cpp:245] Train net output #5: loss/accuracy06 = 0 | |
| I0404 23:42:13.434067 26022 solver.cpp:245] Train net output #6: loss/accuracy07 = 0 |
stas-sl
/ firenet150x50_len22_4_trainval
Last active
April 5, 2016 19:56
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| I0405 13:48:26.366220 29564 solver.cpp:280] Solving | |
| I0405 13:48:26.366231 29564 solver.cpp:281] Learning Rate Policy: poly | |
| I0405 13:48:26.526338 29564 solver.cpp:229] Iteration 0, loss = 4.30412 | |
| I0405 13:48:26.526379 29564 solver.cpp:245] Train net output #0: loss/accuracy01 = 0 | |
| I0405 13:48:26.526397 29564 solver.cpp:245] Train net output #1: loss/accuracy02 = 0.03125 | |
| I0405 13:48:26.526412 29564 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0405 13:48:26.526423 29564 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0405 13:48:26.526435 29564 solver.cpp:245] Train net output #4: loss/accuracy05 = 0.03125 | |
| I0405 13:48:26.526448 29564 solver.cpp:245] Train net output #5: loss/accuracy06 = 0 | |
| I0405 13:48:26.526459 29564 solver.cpp:245] Train net output #6: loss/accuracy07 = 0 |
stas-sl
/ firenet150x50_len22_5_trainval
Last active
April 7, 2016 12:11
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| I0407 12:20:09.825912 32304 solver.cpp:280] Solving | |
| I0407 12:20:09.825924 32304 solver.cpp:281] Learning Rate Policy: poly | |
| I0407 12:20:10.069551 32304 solver.cpp:229] Iteration 0, loss = 4.30406 | |
| I0407 12:20:10.069617 32304 solver.cpp:245] Train net output #0: loss/accuracy01 = 0 | |
| I0407 12:20:10.069638 32304 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0407 12:20:10.069651 32304 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0407 12:20:10.069664 32304 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0407 12:20:10.069674 32304 solver.cpp:245] Train net output #4: loss/accuracy05 = 0 | |
| I0407 12:20:10.069711 32304 solver.cpp:245] Train net output #5: loss/accuracy06 = 0 | |
| I0407 12:20:10.069725 32304 solver.cpp:245] Train net output #6: loss/accuracy07 = 0 |
stas-sl
/ firenet150x50_len22_6_trainval
Last active
April 8, 2016 06:23
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| I0407 15:14:50.542440 1004 solver.cpp:280] Solving | |
| I0407 15:14:50.542451 1004 solver.cpp:281] Learning Rate Policy: poly | |
| I0407 15:14:50.601984 1004 solver.cpp:229] Iteration 0, loss = 4.3042 | |
| I0407 15:14:50.602022 1004 solver.cpp:245] Train net output #0: loss/accuracy01 = 0 | |
| I0407 15:14:50.602041 1004 solver.cpp:245] Train net output #1: loss/accuracy02 = 0 | |
| I0407 15:14:50.602053 1004 solver.cpp:245] Train net output #2: loss/accuracy03 = 0 | |
| I0407 15:14:50.602068 1004 solver.cpp:245] Train net output #3: loss/accuracy04 = 0 | |
| I0407 15:14:50.602082 1004 solver.cpp:245] Train net output #4: loss/accuracy05 = 0 | |
| I0407 15:14:50.602092 1004 solver.cpp:245] Train net output #5: loss/accuracy06 = 0 | |
| I0407 15:14:50.602120 1004 solver.cpp:245] Train net output #6: loss/accuracy07 = 0 |
stas-sl
/ mixed_lstm10_bn
Last active
April 19, 2016 15:17
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| I0407 23:54:15.468787 3443 solver.cpp:280] Solving mixed_lstm | |
| I0407 23:54:15.468801 3443 solver.cpp:281] Learning Rate Policy: poly | |
| I0407 23:54:16.245584 3443 solver.cpp:229] Iteration 0, loss = 13.8505 | |
| I0407 23:54:16.245641 3443 solver.cpp:245] Train net output #0: loss1/accuracy = 0.0208333 | |
| I0407 23:54:16.245658 3443 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0.00568182 | |
| I0407 23:54:16.245672 3443 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.0416667 | |
| I0407 23:54:16.245688 3443 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 4.34597 (* 0.3 = 1.30379 loss) | |
| I0407 23:54:16.245735 3443 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 4.31917 (* 0.3 = 1.29575 loss) | |
| I0407 23:54:16.245749 3443 solver.cpp:245] Train net output #5: loss2/accuracy = 0 | |
| I0407 23:54:16.245761 3443 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0 |
stas-sl
/ mixed_lstm10_bn_2
Created
April 22, 2016 21:04
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| I0421 23:25:34.773021 32397 solver.cpp:280] Solving mixed_lstm | |
| I0421 23:25:34.773036 32397 solver.cpp:281] Learning Rate Policy: fixed | |
| I0421 23:25:35.594626 32397 solver.cpp:229] Iteration 0, loss = 2.77223 | |
| I0421 23:25:35.594671 32397 solver.cpp:245] Train net output #0: loss1/accuracy = 0.528302 | |
| I0421 23:25:35.594687 32397 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0.875 | |
| I0421 23:25:35.594701 32397 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0.625 | |
| I0421 23:25:35.594712 32397 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0.25 | |
| I0421 23:25:35.594724 32397 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0.375 | |
| I0421 23:25:35.594737 32397 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0 | |
| I0421 23:25:35.594748 32397 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0.625 |
stas-sl
/ mixed_lstm11_bn
Last active
April 27, 2016 06:45
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| I0425 10:06:42.668747 22523 solver.cpp:280] Solving mixed_lstm | |
| I0425 10:06:42.668761 22523 solver.cpp:281] Learning Rate Policy: step | |
| I0425 10:06:42.687485 22523 solver.cpp:338] Iteration 0, Testing net (#0) | |
| I0425 10:07:34.893544 22523 solver.cpp:393] Test loss: 1.17466 | |
| I0425 10:07:34.894027 22523 solver.cpp:406] Test net output #0: loss1/accuracy = 0.822526 | |
| I0425 10:07:34.894048 22523 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.92 | |
| I0425 10:07:34.894062 22523 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.723 | |
| I0425 10:07:34.894073 22523 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.567 | |
| I0425 10:07:34.894085 22523 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.553 | |
| I0425 10:07:34.894098 22523 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.616 |