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WIP: data from experimenting with dataset #6

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WIP: data from experimenting with dataset #6

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61 changes: 61 additions & 0 deletions expe_both/kraken.log
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┏━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃ Name ┃ Type ┃ Params ┃ In sizes ┃ Out sizes ┃
┡━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 0 │ val_cer │ CharErrorRate │ 0 │ ? │ ? │
│ 1 │ net │ MultiParamSequential │ 4.0 M │ [[1, 1, 120, 400], '?'] │ [[1, 121, 1, 50], '?'] │
│ 2 │ net.C_0 │ ActConv2D │ 1.3 K │ [[1, 1, 120, 400], '?'] │ [[1, 32, 120, 400], '?'] │
│ 3 │ net.Do_1 │ Dropout │ 0 │ [[1, 32, 120, 400], '?'] │ [[1, 32, 120, 400], '?'] │
│ 4 │ net.Mp_2 │ MaxPool │ 0 │ [[1, 32, 120, 400], '?'] │ [[1, 32, 60, 200], '?'] │
│ 5 │ net.C_3 │ ActConv2D │ 40.0 K │ [[1, 32, 60, 200], '?'] │ [[1, 32, 60, 200], '?'] │
│ 6 │ net.Do_4 │ Dropout │ 0 │ [[1, 32, 60, 200], '?'] │ [[1, 32, 60, 200], '?'] │
│ 7 │ net.Mp_5 │ MaxPool │ 0 │ [[1, 32, 60, 200], '?'] │ [[1, 32, 30, 100], '?'] │
│ 8 │ net.C_6 │ ActConv2D │ 55.4 K │ [[1, 32, 30, 100], '?'] │ [[1, 64, 30, 100], '?'] │
│ 9 │ net.Do_7 │ Dropout │ 0 │ [[1, 64, 30, 100], '?'] │ [[1, 64, 30, 100], '?'] │
│ 10 │ net.Mp_8 │ MaxPool │ 0 │ [[1, 64, 30, 100], '?'] │ [[1, 64, 15, 50], '?'] │
│ 11 │ net.C_9 │ ActConv2D │ 110 K │ [[1, 64, 15, 50], '?'] │ [[1, 64, 15, 50], '?'] │
│ 12 │ net.Do_10 │ Dropout │ 0 │ [[1, 64, 15, 50], '?'] │ [[1, 64, 15, 50], '?'] │
│ 13 │ net.S_11 │ Reshape │ 0 │ [[1, 64, 15, 50], '?'] │ [[1, 960, 1, 50], '?'] │
│ 14 │ net.L_12 │ TransposedSummarizingRNN │ 1.9 M │ [[1, 960, 1, 50], '?'] │ [[1, 400, 1, 50], '?'] │
│ 15 │ net.Do_13 │ Dropout │ 0 │ [[1, 400, 1, 50], '?'] │ [[1, 400, 1, 50], '?'] │
│ 16 │ net.L_14 │ TransposedSummarizingRNN │ 963 K │ [[1, 400, 1, 50], '?'] │ [[1, 400, 1, 50], '?'] │
│ 17 │ net.Do_15 │ Dropout │ 0 │ [[1, 400, 1, 50], '?'] │ [[1, 400, 1, 50], '?'] │
│ 18 │ net.L_16 │ TransposedSummarizingRNN │ 963 K │ [[1, 400, 1, 50], '?'] │ [[1, 400, 1, 50], '?'] │
│ 19 │ net.Do_17 │ Dropout │ 0 │ [[1, 400, 1, 50], '?'] │ [[1, 400, 1, 50], '?'] │
│ 20 │ net.O_18 │ LinSoftmax │ 48.5 K │ [[1, 400, 1, 50], '?'] │ [[1, 121, 1, 50], '?'] │
└────┴───────────┴──────────────────────────┴────────┴──────────────────────────┴──────────────────────────┘
Trainable params: 4.0 M
Non-trainable params: 0
Total params: 4.0 M
Total estimated model params size (MB): 16
stage 0/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:40 val_accuracy: 0.82457 early_stopping: 0/10 0.82457
stage 1/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:40 val_accuracy: 0.84399 early_stopping: 0/10 0.84399
stage 2/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:40 val_accuracy: 0.84616 early_stopping: 0/10 0.84616
stage 3/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:40 val_accuracy: 0.86558 early_stopping: 0/10 0.86558
stage 4/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:39 val_accuracy: 0.86369 early_stopping: 1/10 0.86558
stage 5/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:40 val_accuracy: 0.89819 early_stopping: 0/10 0.89819
stage 6/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:40 val_accuracy: 0.89097 early_stopping: 1/10 0.89819
stage 7/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:40 val_accuracy: 0.89955 early_stopping: 0/10 0.89955
stage 8/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:39 val_accuracy: 0.90316 early_stopping: 0/10 0.90316
stage 9/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:40 val_accuracy: 0.89892 early_stopping: 1/10 0.90316
stage 10/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:40 val_accuracy: 0.89205 early_stopping: 2/10 0.90316
stage 11/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:41 val_accuracy: 0.88889 early_stopping: 3/10 0.90316
stage 12/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.90506 early_stopping: 0/10 0.90506
stage 13/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.91364 early_stopping: 0/10 0.91364
stage 14/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.90108 early_stopping: 1/10 0.91364
stage 15/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.90199 early_stopping: 2/10 0.91364
stage 16/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:43 val_accuracy: 0.90867 early_stopping: 3/10 0.91364
stage 17/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.91418 early_stopping: 0/10 0.91418
stage 18/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.90687 early_stopping: 1/10 0.91418
stage 19/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.89494 early_stopping: 2/10 0.91418
stage 20/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:43 val_accuracy: 0.92385 early_stopping: 0/10 0.92385
stage 21/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:43 val_accuracy: 0.90262 early_stopping: 1/10 0.92385
stage 22/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:43 val_accuracy: 0.90750 early_stopping: 2/10 0.92385
stage 23/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.91518 early_stopping: 3/10 0.92385
stage 24/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:43 val_accuracy: 0.90081 early_stopping: 4/10 0.92385
stage 25/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.91048 early_stopping: 5/10 0.92385
stage 26/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.91915 early_stopping: 6/10 0.92385
stage 27/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.90018 early_stopping: 7/10 0.92385
stage 28/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.90696 early_stopping: 8/10 0.92385
stage 29/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.89756 early_stopping: 9/10 0.92385
stage 30/∞ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 569/569 0:00:00 0:00:42 val_accuracy: 0.90903 early_stopping: 10/10 0.92385
Moving best model /home/ROCQ/almanach/achague/peraire/peraire-ground-truth/models/peraire2_ft_MMCFR_20.mlmodel (0.9238482713699341) to /home/ROCQ/almanach/achague/peraire/peraire-ground-truth/models/peraire2_ft_MMCFR_best.mlmodel
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