-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcnn1d.out
3162 lines (3162 loc) · 227 KB
/
cnn1d.out
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
nohup: 忽略输入
2022-01-11 16:02:52:INFO:Finish setting logger...
2022-01-11 16:02:52:INFO:==> Training/Evaluation parameters are:
2022-01-11 16:02:52:INFO: Namespace(model_dir='cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42'
2022-01-11 16:02:52:INFO: data_fn=1
2022-01-11 16:02:52:INFO: datatest_fn=1
2022-01-11 16:02:52:INFO: filter_kernel_size=1
2022-01-11 16:02:52:INFO: override_data_cache=False
2022-01-11 16:02:52:INFO: maxRUL=125
2022-01-11 16:02:52:INFO: low_ratio=0.1
2022-01-11 16:02:52:INFO: high_ratio=0.99
2022-01-11 16:02:52:INFO: aug_ratio=150
2022-01-11 16:02:52:INFO: noise_amplitude=0.01
2022-01-11 16:02:52:INFO: modeltype='cnn1d'
2022-01-11 16:02:52:INFO: max_seq_len=550
2022-01-11 16:02:52:INFO: d_model=128
2022-01-11 16:02:52:INFO: p_dropout=0.1
2022-01-11 16:02:52:INFO: n_head=4
2022-01-11 16:02:52:INFO: n_layer=2
2022-01-11 16:02:52:INFO: dim_feedforward=512
2022-01-11 16:02:52:INFO: e_dropout=0.1
2022-01-11 16:02:52:INFO: activation='relu'
2022-01-11 16:02:52:INFO: layer_norm=False
2022-01-11 16:02:52:INFO: support_size=0
2022-01-11 16:02:52:INFO: inner_steps=1
2022-01-11 16:02:52:INFO: lr_inner=0.0001
2022-01-11 16:02:52:INFO: lr_meta=0.001
2022-01-11 16:02:52:INFO: n_epochs=5
2022-01-11 16:02:52:INFO: train_batch_size=20
2022-01-11 16:02:52:INFO: eval_batch_size=1
2022-01-11 16:02:52:INFO: lr=0.001
2022-01-11 16:02:52:INFO: weight_decay=0.01
2022-01-11 16:02:52:INFO: warmup_ratio=0.0
2022-01-11 16:02:52:INFO: max_grad_norm=5.0
2022-01-11 16:02:52:INFO: logging_steps=50
2022-01-11 16:02:52:INFO: seed=42
2022-01-11 16:02:52:INFO: gpu_id=3
2022-01-11 16:02:52:INFO: do_train=True
2022-01-11 16:02:52:INFO: do_eval=False
2022-01-11 16:02:52:INFO: train_data_fn='data/train_FD001.txt'
2022-01-11 16:02:52:INFO: test_data_fn='data/test_FD001.txt'
2022-01-11 16:02:52:INFO: target_ruls_fn='data/RUL_FD001.txt'
2022-01-11 16:02:52:INFO: device=device(type='cuda'))
2022-01-11 16:02:52:INFO:Dump arguments to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 16:02:52:INFO:==> Read data from data/train_FD001.txt...
2022-01-11 16:02:52:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 16:02:53:INFO:==> Min_max normalization...
2022-01-11 16:02:53:INFO: The min value is [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 16:02:53:INFO: The max value is [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 16:02:53:INFO:==> Read data from data/test_FD001.txt...
2022-01-11 16:02:53:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 16:02:53:INFO:==> Read RULsfrom data/RUL_FD001.txt...
2022-01-11 16:02:53:INFO: min_rul: 7, max_rul: 145
2022-01-11 16:02:53:INFO:==> Input length ratio of the [TEST] data:
2022-01-11 16:02:53:INFO: min_ratio = 0.2067
2022-01-11 16:02:53:INFO: max_ratio = 0.9667
2022-01-11 16:02:53:INFO:==> Min_max normalization...
2022-01-11 16:02:53:INFO: With given min value [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 16:02:53:INFO: With given max value [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 16:02:57:INFO:Note: NumExpr detected 40 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
2022-01-11 16:02:57:INFO:NumExpr defaulting to 8 threads.
2022-01-11 16:02:57:INFO:=============== Scheme: Normal Learning ===============
2022-01-11 16:02:57:INFO: Num examples = 15000
2022-01-11 16:02:57:INFO: Num epochs = 5
2022-01-11 16:02:57:INFO: Batch size = 20
2022-01-11 16:02:57:INFO: Total optimization steps = 3750
2022-01-11 16:03:05:INFO:==> Group parameters for optimization...
2022-01-11 16:03:05:INFO: Parameters to update are:
2022-01-11 16:03:05:INFO: conv1.0.weight
2022-01-11 16:03:05:INFO: conv2.0.weight
2022-01-11 16:03:05:INFO: conv3.0.weight
2022-01-11 16:03:05:INFO: conv4.0.weight
2022-01-11 16:03:05:INFO: conv5.0.weight
2022-01-11 16:03:05:INFO: fc_1.0.weight
2022-01-11 16:03:05:INFO: fc_1.0.bias
2022-01-11 16:03:05:INFO: fc_2.weight
2022-01-11 16:03:05:INFO: fc_2.bias
/data/moy20/Meta-Learning/Meta-prognosis-main/optimizer.py:78: UserWarning: This overload of add_ is deprecated:
add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other, *, Number alpha) (Triggered internally at /opt/conda/conda-bld/pytorch_1623448255797/work/torch/csrc/utils/python_arg_parser.cpp:1025.)
exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
/data/moy20/miniconda3/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:247: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
warnings.warn("To get the last learning rate computed by the scheduler, "
2022-01-11 16:03:07:INFO:Epoch: 0 global_step: 0/3750 lr: 0.00100 loss: 0.0017
2022-01-11 16:03:26:INFO:Epoch: 0 global_step: 50/3750 lr: 0.00099 loss: 0.0245
2022-01-11 16:03:44:INFO:Epoch: 0 global_step: 100/3750 lr: 0.00097 loss: 0.0077
2022-01-11 16:04:03:INFO:Epoch: 0 global_step: 150/3750 lr: 0.00096 loss: 0.0070
2022-01-11 16:04:22:INFO:Epoch: 0 global_step: 200/3750 lr: 0.00095 loss: 0.0062
2022-01-11 16:04:41:INFO:Epoch: 0 global_step: 250/3750 lr: 0.00093 loss: 0.0056
2022-01-11 16:04:59:INFO:Epoch: 0 global_step: 300/3750 lr: 0.00092 loss: 0.0049
2022-01-11 16:05:18:INFO:Epoch: 0 global_step: 350/3750 lr: 0.00091 loss: 0.0039
2022-01-11 16:05:36:INFO:Epoch: 0 global_step: 400/3750 lr: 0.00089 loss: 0.0036
2022-01-11 16:05:55:INFO:Epoch: 0 global_step: 450/3750 lr: 0.00088 loss: 0.0037
2022-01-11 16:06:14:INFO:Epoch: 0 global_step: 500/3750 lr: 0.00087 loss: 0.0034
2022-01-11 16:06:32:INFO:Epoch: 0 global_step: 550/3750 lr: 0.00085 loss: 0.0032
2022-01-11 16:06:51:INFO:Epoch: 0 global_step: 600/3750 lr: 0.00084 loss: 0.0028
2022-01-11 16:07:10:INFO:Epoch: 0 global_step: 650/3750 lr: 0.00083 loss: 0.0030
2022-01-11 16:07:28:INFO:Epoch: 0 global_step: 700/3750 lr: 0.00081 loss: 0.0029
2022-01-11 16:07:49:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:07:49:INFO: Num examples = 100
2022-01-11 16:07:49:INFO: RMSE = 34.3841
2022-01-11 16:07:51:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:07:51:INFO: Num examples = 100
2022-01-11 16:07:51:INFO: RMSE = 33.1105
2022-01-11 16:07:51:INFO:==> Minimal valid RMSE!
2022-01-11 16:07:51:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 16:07:51:INFO:Epoch: 1 global_step: 750/3750 lr: 0.00080 loss: 0.0029
2022-01-11 16:08:10:INFO:Epoch: 1 global_step: 800/3750 lr: 0.00079 loss: 0.0027
2022-01-11 16:08:28:INFO:Epoch: 1 global_step: 850/3750 lr: 0.00077 loss: 0.0028
2022-01-11 16:08:47:INFO:Epoch: 1 global_step: 900/3750 lr: 0.00076 loss: 0.0022
2022-01-11 16:09:05:INFO:Epoch: 1 global_step: 950/3750 lr: 0.00075 loss: 0.0025
2022-01-11 16:09:24:INFO:Epoch: 1 global_step: 1000/3750 lr: 0.00073 loss: 0.0023
2022-01-11 16:09:43:INFO:Epoch: 1 global_step: 1050/3750 lr: 0.00072 loss: 0.0025
2022-01-11 16:10:01:INFO:Epoch: 1 global_step: 1100/3750 lr: 0.00071 loss: 0.0023
2022-01-11 16:10:20:INFO:Epoch: 1 global_step: 1150/3750 lr: 0.00069 loss: 0.0024
2022-01-11 16:10:39:INFO:Epoch: 1 global_step: 1200/3750 lr: 0.00068 loss: 0.0021
2022-01-11 16:10:57:INFO:Epoch: 1 global_step: 1250/3750 lr: 0.00067 loss: 0.0025
2022-01-11 16:11:16:INFO:Epoch: 1 global_step: 1300/3750 lr: 0.00065 loss: 0.0020
2022-01-11 16:11:34:INFO:Epoch: 1 global_step: 1350/3750 lr: 0.00064 loss: 0.0020
2022-01-11 16:11:53:INFO:Epoch: 1 global_step: 1400/3750 lr: 0.00063 loss: 0.0020
2022-01-11 16:12:11:INFO:Epoch: 1 global_step: 1450/3750 lr: 0.00061 loss: 0.0018
2022-01-11 16:12:32:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:12:32:INFO: Num examples = 100
2022-01-11 16:12:32:INFO: RMSE = 33.5590
2022-01-11 16:12:34:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:12:34:INFO: Num examples = 100
2022-01-11 16:12:34:INFO: RMSE = 24.9395
2022-01-11 16:12:34:INFO:==> Minimal valid RMSE!
2022-01-11 16:12:34:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 16:12:34:INFO:Epoch: 2 global_step: 1500/3750 lr: 0.00060 loss: 0.0019
2022-01-11 16:12:53:INFO:Epoch: 2 global_step: 1550/3750 lr: 0.00059 loss: 0.0021
2022-01-11 16:13:12:INFO:Epoch: 2 global_step: 1600/3750 lr: 0.00057 loss: 0.0018
2022-01-11 16:13:30:INFO:Epoch: 2 global_step: 1650/3750 lr: 0.00056 loss: 0.0020
2022-01-11 16:13:49:INFO:Epoch: 2 global_step: 1700/3750 lr: 0.00055 loss: 0.0021
2022-01-11 16:14:08:INFO:Epoch: 2 global_step: 1750/3750 lr: 0.00053 loss: 0.0019
2022-01-11 16:14:26:INFO:Epoch: 2 global_step: 1800/3750 lr: 0.00052 loss: 0.0019
2022-01-11 16:14:44:INFO:Epoch: 2 global_step: 1850/3750 lr: 0.00051 loss: 0.0018
2022-01-11 16:15:03:INFO:Epoch: 2 global_step: 1900/3750 lr: 0.00049 loss: 0.0017
2022-01-11 16:15:21:INFO:Epoch: 2 global_step: 1950/3750 lr: 0.00048 loss: 0.0017
2022-01-11 16:15:40:INFO:Epoch: 2 global_step: 2000/3750 lr: 0.00047 loss: 0.0018
2022-01-11 16:15:59:INFO:Epoch: 2 global_step: 2050/3750 lr: 0.00045 loss: 0.0018
2022-01-11 16:16:17:INFO:Epoch: 2 global_step: 2100/3750 lr: 0.00044 loss: 0.0017
2022-01-11 16:16:36:INFO:Epoch: 2 global_step: 2150/3750 lr: 0.00043 loss: 0.0017
2022-01-11 16:16:55:INFO:Epoch: 2 global_step: 2200/3750 lr: 0.00041 loss: 0.0015
2022-01-11 16:17:15:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:17:15:INFO: Num examples = 100
2022-01-11 16:17:15:INFO: RMSE = 31.6601
2022-01-11 16:17:17:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:17:17:INFO: Num examples = 100
2022-01-11 16:17:17:INFO: RMSE = 22.9304
2022-01-11 16:17:17:INFO:==> Minimal valid RMSE!
2022-01-11 16:17:17:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 16:17:18:INFO:Epoch: 3 global_step: 2250/3750 lr: 0.00040 loss: 0.0016
2022-01-11 16:17:36:INFO:Epoch: 3 global_step: 2300/3750 lr: 0.00039 loss: 0.0015
2022-01-11 16:17:55:INFO:Epoch: 3 global_step: 2350/3750 lr: 0.00037 loss: 0.0016
2022-01-11 16:18:13:INFO:Epoch: 3 global_step: 2400/3750 lr: 0.00036 loss: 0.0016
2022-01-11 16:18:32:INFO:Epoch: 3 global_step: 2450/3750 lr: 0.00035 loss: 0.0015
2022-01-11 16:18:51:INFO:Epoch: 3 global_step: 2500/3750 lr: 0.00033 loss: 0.0014
2022-01-11 16:19:09:INFO:Epoch: 3 global_step: 2550/3750 lr: 0.00032 loss: 0.0014
2022-01-11 16:19:28:INFO:Epoch: 3 global_step: 2600/3750 lr: 0.00031 loss: 0.0014
2022-01-11 16:19:46:INFO:Epoch: 3 global_step: 2650/3750 lr: 0.00029 loss: 0.0015
2022-01-11 16:20:05:INFO:Epoch: 3 global_step: 2700/3750 lr: 0.00028 loss: 0.0013
2022-01-11 16:20:24:INFO:Epoch: 3 global_step: 2750/3750 lr: 0.00027 loss: 0.0013
2022-01-11 16:20:42:INFO:Epoch: 3 global_step: 2800/3750 lr: 0.00025 loss: 0.0013
2022-01-11 16:21:01:INFO:Epoch: 3 global_step: 2850/3750 lr: 0.00024 loss: 0.0013
2022-01-11 16:21:19:INFO:Epoch: 3 global_step: 2900/3750 lr: 0.00023 loss: 0.0013
2022-01-11 16:21:38:INFO:Epoch: 3 global_step: 2950/3750 lr: 0.00021 loss: 0.0013
2022-01-11 16:21:58:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:21:58:INFO: Num examples = 100
2022-01-11 16:21:58:INFO: RMSE = 31.8161
2022-01-11 16:22:01:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:22:01:INFO: Num examples = 100
2022-01-11 16:22:01:INFO: RMSE = 28.6948
2022-01-11 16:22:01:INFO:Epoch: 4 global_step: 3000/3750 lr: 0.00020 loss: 0.0013
2022-01-11 16:22:20:INFO:Epoch: 4 global_step: 3050/3750 lr: 0.00019 loss: 0.0012
2022-01-11 16:22:38:INFO:Epoch: 4 global_step: 3100/3750 lr: 0.00017 loss: 0.0011
2022-01-11 16:22:57:INFO:Epoch: 4 global_step: 3150/3750 lr: 0.00016 loss: 0.0010
2022-01-11 16:23:15:INFO:Epoch: 4 global_step: 3200/3750 lr: 0.00015 loss: 0.0013
2022-01-11 16:23:34:INFO:Epoch: 4 global_step: 3250/3750 lr: 0.00013 loss: 0.0012
2022-01-11 16:23:53:INFO:Epoch: 4 global_step: 3300/3750 lr: 0.00012 loss: 0.0011
2022-01-11 16:24:11:INFO:Epoch: 4 global_step: 3350/3750 lr: 0.00011 loss: 0.0012
2022-01-11 16:24:30:INFO:Epoch: 4 global_step: 3400/3750 lr: 0.00009 loss: 0.0013
2022-01-11 16:24:49:INFO:Epoch: 4 global_step: 3450/3750 lr: 0.00008 loss: 0.0012
2022-01-11 16:25:07:INFO:Epoch: 4 global_step: 3500/3750 lr: 0.00007 loss: 0.0012
2022-01-11 16:25:26:INFO:Epoch: 4 global_step: 3550/3750 lr: 0.00005 loss: 0.0012
2022-01-11 16:25:45:INFO:Epoch: 4 global_step: 3600/3750 lr: 0.00004 loss: 0.0012
2022-01-11 16:26:03:INFO:Epoch: 4 global_step: 3650/3750 lr: 0.00003 loss: 0.0013
2022-01-11 16:26:22:INFO:Epoch: 4 global_step: 3700/3750 lr: 0.00001 loss: 0.0011
2022-01-11 16:26:42:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:26:42:INFO: Num examples = 100
2022-01-11 16:26:42:INFO: RMSE = 30.9256
2022-01-11 16:26:45:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:26:45:INFO: Num examples = 100
2022-01-11 16:26:45:INFO: RMSE = 25.8115
2022-01-11 16:26:45:INFO: Output TEST RMSE: 31.6601
2022-01-11 16:26:45:INFO: VALID RMSEs: 33.1105 24.9395 22.9304 28.6948 25.8115
2022-01-11 16:26:45:INFO: TEST RMSEs: 34.3841 33.5590 31.6601 31.8161 30.9256
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
2022-01-11 16:26:47:INFO:Finish setting logger...
2022-01-11 16:26:47:INFO:==> Training/Evaluation parameters are:
2022-01-11 16:26:47:INFO: Namespace(model_dir='cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-667'
2022-01-11 16:26:47:INFO: data_fn=1
2022-01-11 16:26:47:INFO: datatest_fn=1
2022-01-11 16:26:47:INFO: filter_kernel_size=1
2022-01-11 16:26:47:INFO: override_data_cache=False
2022-01-11 16:26:47:INFO: maxRUL=125
2022-01-11 16:26:47:INFO: low_ratio=0.1
2022-01-11 16:26:47:INFO: high_ratio=0.99
2022-01-11 16:26:47:INFO: aug_ratio=150
2022-01-11 16:26:47:INFO: noise_amplitude=0.01
2022-01-11 16:26:47:INFO: modeltype='cnn1d'
2022-01-11 16:26:47:INFO: max_seq_len=550
2022-01-11 16:26:47:INFO: d_model=128
2022-01-11 16:26:47:INFO: p_dropout=0.1
2022-01-11 16:26:47:INFO: n_head=4
2022-01-11 16:26:47:INFO: n_layer=2
2022-01-11 16:26:47:INFO: dim_feedforward=512
2022-01-11 16:26:47:INFO: e_dropout=0.1
2022-01-11 16:26:47:INFO: activation='relu'
2022-01-11 16:26:47:INFO: layer_norm=False
2022-01-11 16:26:47:INFO: support_size=0
2022-01-11 16:26:47:INFO: inner_steps=1
2022-01-11 16:26:47:INFO: lr_inner=0.0001
2022-01-11 16:26:47:INFO: lr_meta=0.001
2022-01-11 16:26:47:INFO: n_epochs=5
2022-01-11 16:26:47:INFO: train_batch_size=20
2022-01-11 16:26:47:INFO: eval_batch_size=1
2022-01-11 16:26:47:INFO: lr=0.001
2022-01-11 16:26:47:INFO: weight_decay=0.01
2022-01-11 16:26:47:INFO: warmup_ratio=0.0
2022-01-11 16:26:47:INFO: max_grad_norm=5.0
2022-01-11 16:26:47:INFO: logging_steps=50
2022-01-11 16:26:47:INFO: seed=667
2022-01-11 16:26:47:INFO: gpu_id=3
2022-01-11 16:26:47:INFO: do_train=True
2022-01-11 16:26:47:INFO: do_eval=False
2022-01-11 16:26:47:INFO: train_data_fn='data/train_FD001.txt'
2022-01-11 16:26:47:INFO: test_data_fn='data/test_FD001.txt'
2022-01-11 16:26:47:INFO: target_ruls_fn='data/RUL_FD001.txt'
2022-01-11 16:26:47:INFO: device=device(type='cuda'))
2022-01-11 16:26:47:INFO:Dump arguments to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-667...
2022-01-11 16:26:47:INFO:==> Read data from data/train_FD001.txt...
2022-01-11 16:26:47:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 16:26:48:INFO:==> Min_max normalization...
2022-01-11 16:26:48:INFO: The min value is [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 16:26:48:INFO: The max value is [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 16:26:48:INFO:==> Read data from data/test_FD001.txt...
2022-01-11 16:26:48:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 16:26:48:INFO:==> Read RULsfrom data/RUL_FD001.txt...
2022-01-11 16:26:48:INFO: min_rul: 7, max_rul: 145
2022-01-11 16:26:48:INFO:==> Input length ratio of the [TEST] data:
2022-01-11 16:26:48:INFO: min_ratio = 0.2067
2022-01-11 16:26:48:INFO: max_ratio = 0.9667
2022-01-11 16:26:48:INFO:==> Min_max normalization...
2022-01-11 16:26:48:INFO: With given min value [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 16:26:48:INFO: With given max value [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 16:26:51:INFO:Note: NumExpr detected 40 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
2022-01-11 16:26:51:INFO:NumExpr defaulting to 8 threads.
2022-01-11 16:26:52:INFO:=============== Scheme: Normal Learning ===============
2022-01-11 16:26:52:INFO: Num examples = 15000
2022-01-11 16:26:52:INFO: Num epochs = 5
2022-01-11 16:26:52:INFO: Batch size = 20
2022-01-11 16:26:52:INFO: Total optimization steps = 3750
2022-01-11 16:27:00:INFO:==> Group parameters for optimization...
2022-01-11 16:27:00:INFO: Parameters to update are:
2022-01-11 16:27:00:INFO: conv1.0.weight
2022-01-11 16:27:00:INFO: conv2.0.weight
2022-01-11 16:27:00:INFO: conv3.0.weight
2022-01-11 16:27:00:INFO: conv4.0.weight
2022-01-11 16:27:00:INFO: conv5.0.weight
2022-01-11 16:27:00:INFO: fc_1.0.weight
2022-01-11 16:27:00:INFO: fc_1.0.bias
2022-01-11 16:27:00:INFO: fc_2.weight
2022-01-11 16:27:00:INFO: fc_2.bias
/data/moy20/Meta-Learning/Meta-prognosis-main/optimizer.py:78: UserWarning: This overload of add_ is deprecated:
add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other, *, Number alpha) (Triggered internally at /opt/conda/conda-bld/pytorch_1623448255797/work/torch/csrc/utils/python_arg_parser.cpp:1025.)
exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
/data/moy20/miniconda3/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:247: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
warnings.warn("To get the last learning rate computed by the scheduler, "
2022-01-11 16:27:02:INFO:Epoch: 0 global_step: 0/3750 lr: 0.00100 loss: 0.0011
2022-01-11 16:27:21:INFO:Epoch: 0 global_step: 50/3750 lr: 0.00099 loss: 0.0172
2022-01-11 16:27:39:INFO:Epoch: 0 global_step: 100/3750 lr: 0.00097 loss: 0.0073
2022-01-11 16:27:58:INFO:Epoch: 0 global_step: 150/3750 lr: 0.00096 loss: 0.0064
2022-01-11 16:28:17:INFO:Epoch: 0 global_step: 200/3750 lr: 0.00095 loss: 0.0053
2022-01-11 16:28:35:INFO:Epoch: 0 global_step: 250/3750 lr: 0.00093 loss: 0.0049
2022-01-11 16:28:54:INFO:Epoch: 0 global_step: 300/3750 lr: 0.00092 loss: 0.0036
2022-01-11 16:29:13:INFO:Epoch: 0 global_step: 350/3750 lr: 0.00091 loss: 0.0036
2022-01-11 16:29:31:INFO:Epoch: 0 global_step: 400/3750 lr: 0.00089 loss: 0.0031
2022-01-11 16:29:50:INFO:Epoch: 0 global_step: 450/3750 lr: 0.00088 loss: 0.0029
2022-01-11 16:30:08:INFO:Epoch: 0 global_step: 500/3750 lr: 0.00087 loss: 0.0029
2022-01-11 16:30:27:INFO:Epoch: 0 global_step: 550/3750 lr: 0.00085 loss: 0.0029
2022-01-11 16:30:46:INFO:Epoch: 0 global_step: 600/3750 lr: 0.00084 loss: 0.0030
2022-01-11 16:31:04:INFO:Epoch: 0 global_step: 650/3750 lr: 0.00083 loss: 0.0032
2022-01-11 16:31:23:INFO:Epoch: 0 global_step: 700/3750 lr: 0.00081 loss: 0.0025
2022-01-11 16:31:43:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:31:43:INFO: Num examples = 100
2022-01-11 16:31:43:INFO: RMSE = 32.4861
2022-01-11 16:31:45:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:31:45:INFO: Num examples = 100
2022-01-11 16:31:45:INFO: RMSE = 33.3834
2022-01-11 16:31:45:INFO:==> Minimal valid RMSE!
2022-01-11 16:31:45:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-667...
2022-01-11 16:31:46:INFO:Epoch: 1 global_step: 750/3750 lr: 0.00080 loss: 0.0026
2022-01-11 16:32:04:INFO:Epoch: 1 global_step: 800/3750 lr: 0.00079 loss: 0.0024
2022-01-11 16:32:23:INFO:Epoch: 1 global_step: 850/3750 lr: 0.00077 loss: 0.0023
2022-01-11 16:32:42:INFO:Epoch: 1 global_step: 900/3750 lr: 0.00076 loss: 0.0024
2022-01-11 16:33:00:INFO:Epoch: 1 global_step: 950/3750 lr: 0.00075 loss: 0.0023
2022-01-11 16:33:19:INFO:Epoch: 1 global_step: 1000/3750 lr: 0.00073 loss: 0.0022
2022-01-11 16:33:37:INFO:Epoch: 1 global_step: 1050/3750 lr: 0.00072 loss: 0.0022
2022-01-11 16:33:56:INFO:Epoch: 1 global_step: 1100/3750 lr: 0.00071 loss: 0.0024
2022-01-11 16:34:15:INFO:Epoch: 1 global_step: 1150/3750 lr: 0.00069 loss: 0.0022
2022-01-11 16:34:33:INFO:Epoch: 1 global_step: 1200/3750 lr: 0.00068 loss: 0.0025
2022-01-11 16:34:52:INFO:Epoch: 1 global_step: 1250/3750 lr: 0.00067 loss: 0.0022
2022-01-11 16:35:10:INFO:Epoch: 1 global_step: 1300/3750 lr: 0.00065 loss: 0.0020
2022-01-11 16:35:29:INFO:Epoch: 1 global_step: 1350/3750 lr: 0.00064 loss: 0.0023
2022-01-11 16:35:48:INFO:Epoch: 1 global_step: 1400/3750 lr: 0.00063 loss: 0.0018
2022-01-11 16:36:06:INFO:Epoch: 1 global_step: 1450/3750 lr: 0.00061 loss: 0.0020
2022-01-11 16:36:26:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:36:26:INFO: Num examples = 100
2022-01-11 16:36:26:INFO: RMSE = 32.9181
2022-01-11 16:36:29:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:36:29:INFO: Num examples = 100
2022-01-11 16:36:29:INFO: RMSE = 27.1928
2022-01-11 16:36:29:INFO:==> Minimal valid RMSE!
2022-01-11 16:36:29:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-667...
2022-01-11 16:36:29:INFO:Epoch: 2 global_step: 1500/3750 lr: 0.00060 loss: 0.0019
2022-01-11 16:36:47:INFO:Epoch: 2 global_step: 1550/3750 lr: 0.00059 loss: 0.0021
2022-01-11 16:37:06:INFO:Epoch: 2 global_step: 1600/3750 lr: 0.00057 loss: 0.0020
2022-01-11 16:37:25:INFO:Epoch: 2 global_step: 1650/3750 lr: 0.00056 loss: 0.0020
2022-01-11 16:37:43:INFO:Epoch: 2 global_step: 1700/3750 lr: 0.00055 loss: 0.0022
2022-01-11 16:38:02:INFO:Epoch: 2 global_step: 1750/3750 lr: 0.00053 loss: 0.0020
2022-01-11 16:38:21:INFO:Epoch: 2 global_step: 1800/3750 lr: 0.00052 loss: 0.0018
2022-01-11 16:38:39:INFO:Epoch: 2 global_step: 1850/3750 lr: 0.00051 loss: 0.0019
2022-01-11 16:38:58:INFO:Epoch: 2 global_step: 1900/3750 lr: 0.00049 loss: 0.0019
2022-01-11 16:39:16:INFO:Epoch: 2 global_step: 1950/3750 lr: 0.00048 loss: 0.0018
2022-01-11 16:39:35:INFO:Epoch: 2 global_step: 2000/3750 lr: 0.00047 loss: 0.0020
2022-01-11 16:39:53:INFO:Epoch: 2 global_step: 2050/3750 lr: 0.00045 loss: 0.0018
2022-01-11 16:40:12:INFO:Epoch: 2 global_step: 2100/3750 lr: 0.00044 loss: 0.0016
2022-01-11 16:40:31:INFO:Epoch: 2 global_step: 2150/3750 lr: 0.00043 loss: 0.0020
2022-01-11 16:40:49:INFO:Epoch: 2 global_step: 2200/3750 lr: 0.00041 loss: 0.0018
2022-01-11 16:41:10:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:41:10:INFO: Num examples = 100
2022-01-11 16:41:10:INFO: RMSE = 33.4819
2022-01-11 16:41:12:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:41:12:INFO: Num examples = 100
2022-01-11 16:41:12:INFO: RMSE = 30.1326
2022-01-11 16:41:12:INFO:Epoch: 3 global_step: 2250/3750 lr: 0.00040 loss: 0.0020
2022-01-11 16:41:31:INFO:Epoch: 3 global_step: 2300/3750 lr: 0.00039 loss: 0.0018
2022-01-11 16:41:49:INFO:Epoch: 3 global_step: 2350/3750 lr: 0.00037 loss: 0.0016
2022-01-11 16:42:08:INFO:Epoch: 3 global_step: 2400/3750 lr: 0.00036 loss: 0.0016
2022-01-11 16:42:26:INFO:Epoch: 3 global_step: 2450/3750 lr: 0.00035 loss: 0.0017
2022-01-11 16:42:45:INFO:Epoch: 3 global_step: 2500/3750 lr: 0.00033 loss: 0.0017
2022-01-11 16:43:04:INFO:Epoch: 3 global_step: 2550/3750 lr: 0.00032 loss: 0.0017
2022-01-11 16:43:22:INFO:Epoch: 3 global_step: 2600/3750 lr: 0.00031 loss: 0.0016
2022-01-11 16:43:41:INFO:Epoch: 3 global_step: 2650/3750 lr: 0.00029 loss: 0.0016
2022-01-11 16:43:59:INFO:Epoch: 3 global_step: 2700/3750 lr: 0.00028 loss: 0.0015
2022-01-11 16:44:18:INFO:Epoch: 3 global_step: 2750/3750 lr: 0.00027 loss: 0.0016
2022-01-11 16:44:37:INFO:Epoch: 3 global_step: 2800/3750 lr: 0.00025 loss: 0.0016
2022-01-11 16:44:55:INFO:Epoch: 3 global_step: 2850/3750 lr: 0.00024 loss: 0.0015
2022-01-11 16:45:14:INFO:Epoch: 3 global_step: 2900/3750 lr: 0.00023 loss: 0.0017
2022-01-11 16:45:32:INFO:Epoch: 3 global_step: 2950/3750 lr: 0.00021 loss: 0.0015
2022-01-11 16:45:53:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:45:53:INFO: Num examples = 100
2022-01-11 16:45:53:INFO: RMSE = 32.7773
2022-01-11 16:45:55:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:45:55:INFO: Num examples = 100
2022-01-11 16:45:55:INFO: RMSE = 27.2060
2022-01-11 16:45:55:INFO:Epoch: 4 global_step: 3000/3750 lr: 0.00020 loss: 0.0015
2022-01-11 16:46:14:INFO:Epoch: 4 global_step: 3050/3750 lr: 0.00019 loss: 0.0015
2022-01-11 16:46:32:INFO:Epoch: 4 global_step: 3100/3750 lr: 0.00017 loss: 0.0015
2022-01-11 16:46:51:INFO:Epoch: 4 global_step: 3150/3750 lr: 0.00016 loss: 0.0016
2022-01-11 16:47:09:INFO:Epoch: 4 global_step: 3200/3750 lr: 0.00015 loss: 0.0014
2022-01-11 16:47:28:INFO:Epoch: 4 global_step: 3250/3750 lr: 0.00013 loss: 0.0015
2022-01-11 16:47:47:INFO:Epoch: 4 global_step: 3300/3750 lr: 0.00012 loss: 0.0014
2022-01-11 16:48:05:INFO:Epoch: 4 global_step: 3350/3750 lr: 0.00011 loss: 0.0016
2022-01-11 16:48:24:INFO:Epoch: 4 global_step: 3400/3750 lr: 0.00009 loss: 0.0015
2022-01-11 16:48:43:INFO:Epoch: 4 global_step: 3450/3750 lr: 0.00008 loss: 0.0015
2022-01-11 16:49:01:INFO:Epoch: 4 global_step: 3500/3750 lr: 0.00007 loss: 0.0015
2022-01-11 16:49:20:INFO:Epoch: 4 global_step: 3550/3750 lr: 0.00005 loss: 0.0014
2022-01-11 16:49:42:INFO:Epoch: 4 global_step: 3600/3750 lr: 0.00004 loss: 0.0015
2022-01-11 16:50:00:INFO:Epoch: 4 global_step: 3650/3750 lr: 0.00003 loss: 0.0014
2022-01-11 16:50:19:INFO:Epoch: 4 global_step: 3700/3750 lr: 0.00001 loss: 0.0014
2022-01-11 16:50:39:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:50:39:INFO: Num examples = 100
2022-01-11 16:50:39:INFO: RMSE = 32.1514
2022-01-11 16:50:41:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:50:41:INFO: Num examples = 100
2022-01-11 16:50:41:INFO: RMSE = 27.3064
2022-01-11 16:50:41:INFO: Output TEST RMSE: 32.9181
2022-01-11 16:50:41:INFO: VALID RMSEs: 33.3834 27.1928 30.1326 27.2060 27.3064
2022-01-11 16:50:41:INFO: TEST RMSEs: 32.4861 32.9181 33.4819 32.7773 32.1514
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
2022-01-11 16:50:44:INFO:Finish setting logger...
2022-01-11 16:50:44:INFO:==> Training/Evaluation parameters are:
2022-01-11 16:50:44:INFO: Namespace(model_dir='cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-128'
2022-01-11 16:50:44:INFO: data_fn=1
2022-01-11 16:50:44:INFO: datatest_fn=1
2022-01-11 16:50:44:INFO: filter_kernel_size=1
2022-01-11 16:50:44:INFO: override_data_cache=False
2022-01-11 16:50:44:INFO: maxRUL=125
2022-01-11 16:50:44:INFO: low_ratio=0.1
2022-01-11 16:50:44:INFO: high_ratio=0.99
2022-01-11 16:50:44:INFO: aug_ratio=150
2022-01-11 16:50:44:INFO: noise_amplitude=0.01
2022-01-11 16:50:44:INFO: modeltype='cnn1d'
2022-01-11 16:50:44:INFO: max_seq_len=550
2022-01-11 16:50:44:INFO: d_model=128
2022-01-11 16:50:44:INFO: p_dropout=0.1
2022-01-11 16:50:44:INFO: n_head=4
2022-01-11 16:50:44:INFO: n_layer=2
2022-01-11 16:50:44:INFO: dim_feedforward=512
2022-01-11 16:50:44:INFO: e_dropout=0.1
2022-01-11 16:50:44:INFO: activation='relu'
2022-01-11 16:50:44:INFO: layer_norm=False
2022-01-11 16:50:44:INFO: support_size=0
2022-01-11 16:50:44:INFO: inner_steps=1
2022-01-11 16:50:44:INFO: lr_inner=0.0001
2022-01-11 16:50:44:INFO: lr_meta=0.001
2022-01-11 16:50:44:INFO: n_epochs=5
2022-01-11 16:50:44:INFO: train_batch_size=20
2022-01-11 16:50:44:INFO: eval_batch_size=1
2022-01-11 16:50:44:INFO: lr=0.001
2022-01-11 16:50:44:INFO: weight_decay=0.01
2022-01-11 16:50:44:INFO: warmup_ratio=0.0
2022-01-11 16:50:44:INFO: max_grad_norm=5.0
2022-01-11 16:50:44:INFO: logging_steps=50
2022-01-11 16:50:44:INFO: seed=128
2022-01-11 16:50:44:INFO: gpu_id=3
2022-01-11 16:50:44:INFO: do_train=True
2022-01-11 16:50:44:INFO: do_eval=False
2022-01-11 16:50:44:INFO: train_data_fn='data/train_FD001.txt'
2022-01-11 16:50:44:INFO: test_data_fn='data/test_FD001.txt'
2022-01-11 16:50:44:INFO: target_ruls_fn='data/RUL_FD001.txt'
2022-01-11 16:50:44:INFO: device=device(type='cuda'))
2022-01-11 16:50:44:INFO:Dump arguments to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-128...
2022-01-11 16:50:44:INFO:==> Read data from data/train_FD001.txt...
2022-01-11 16:50:44:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 16:50:44:INFO:==> Min_max normalization...
2022-01-11 16:50:44:INFO: The min value is [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 16:50:44:INFO: The max value is [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 16:50:44:INFO:==> Read data from data/test_FD001.txt...
2022-01-11 16:50:44:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 16:50:44:INFO:==> Read RULsfrom data/RUL_FD001.txt...
2022-01-11 16:50:44:INFO: min_rul: 7, max_rul: 145
2022-01-11 16:50:44:INFO:==> Input length ratio of the [TEST] data:
2022-01-11 16:50:44:INFO: min_ratio = 0.2067
2022-01-11 16:50:44:INFO: max_ratio = 0.9667
2022-01-11 16:50:44:INFO:==> Min_max normalization...
2022-01-11 16:50:44:INFO: With given min value [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 16:50:44:INFO: With given max value [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 16:50:48:INFO:Note: NumExpr detected 40 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
2022-01-11 16:50:48:INFO:NumExpr defaulting to 8 threads.
2022-01-11 16:50:48:INFO:=============== Scheme: Normal Learning ===============
2022-01-11 16:50:48:INFO: Num examples = 15000
2022-01-11 16:50:48:INFO: Num epochs = 5
2022-01-11 16:50:48:INFO: Batch size = 20
2022-01-11 16:50:48:INFO: Total optimization steps = 3750
2022-01-11 16:50:57:INFO:==> Group parameters for optimization...
2022-01-11 16:50:57:INFO: Parameters to update are:
2022-01-11 16:50:57:INFO: conv1.0.weight
2022-01-11 16:50:57:INFO: conv2.0.weight
2022-01-11 16:50:57:INFO: conv3.0.weight
2022-01-11 16:50:57:INFO: conv4.0.weight
2022-01-11 16:50:57:INFO: conv5.0.weight
2022-01-11 16:50:57:INFO: fc_1.0.weight
2022-01-11 16:50:57:INFO: fc_1.0.bias
2022-01-11 16:50:57:INFO: fc_2.weight
2022-01-11 16:50:57:INFO: fc_2.bias
/data/moy20/Meta-Learning/Meta-prognosis-main/optimizer.py:78: UserWarning: This overload of add_ is deprecated:
add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other, *, Number alpha) (Triggered internally at /opt/conda/conda-bld/pytorch_1623448255797/work/torch/csrc/utils/python_arg_parser.cpp:1025.)
exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
/data/moy20/miniconda3/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:247: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
warnings.warn("To get the last learning rate computed by the scheduler, "
2022-01-11 16:50:59:INFO:Epoch: 0 global_step: 0/3750 lr: 0.00100 loss: 0.0013
2022-01-11 16:51:17:INFO:Epoch: 0 global_step: 50/3750 lr: 0.00099 loss: 0.0252
2022-01-11 16:51:36:INFO:Epoch: 0 global_step: 100/3750 lr: 0.00097 loss: 0.0094
2022-01-11 16:51:54:INFO:Epoch: 0 global_step: 150/3750 lr: 0.00096 loss: 0.0051
2022-01-11 16:52:13:INFO:Epoch: 0 global_step: 200/3750 lr: 0.00095 loss: 0.0038
2022-01-11 16:52:32:INFO:Epoch: 0 global_step: 250/3750 lr: 0.00093 loss: 0.0039
2022-01-11 16:52:50:INFO:Epoch: 0 global_step: 300/3750 lr: 0.00092 loss: 0.0033
2022-01-11 16:53:09:INFO:Epoch: 0 global_step: 350/3750 lr: 0.00091 loss: 0.0031
2022-01-11 16:53:28:INFO:Epoch: 0 global_step: 400/3750 lr: 0.00089 loss: 0.0027
2022-01-11 16:53:46:INFO:Epoch: 0 global_step: 450/3750 lr: 0.00088 loss: 0.0026
2022-01-11 16:54:05:INFO:Epoch: 0 global_step: 500/3750 lr: 0.00087 loss: 0.0024
2022-01-11 16:54:23:INFO:Epoch: 0 global_step: 550/3750 lr: 0.00085 loss: 0.0025
2022-01-11 16:54:42:INFO:Epoch: 0 global_step: 600/3750 lr: 0.00084 loss: 0.0023
2022-01-11 16:55:01:INFO:Epoch: 0 global_step: 650/3750 lr: 0.00083 loss: 0.0025
2022-01-11 16:55:19:INFO:Epoch: 0 global_step: 700/3750 lr: 0.00081 loss: 0.0025
2022-01-11 16:55:40:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 16:55:40:INFO: Num examples = 100
2022-01-11 16:55:40:INFO: RMSE = 31.0949
2022-01-11 16:55:42:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 16:55:42:INFO: Num examples = 100
2022-01-11 16:55:42:INFO: RMSE = 31.5513
2022-01-11 16:55:42:INFO:==> Minimal valid RMSE!
2022-01-11 16:55:42:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-128...
2022-01-11 16:55:42:INFO:Epoch: 1 global_step: 750/3750 lr: 0.00080 loss: 0.0026
2022-01-11 16:56:01:INFO:Epoch: 1 global_step: 800/3750 lr: 0.00079 loss: 0.0025
2022-01-11 16:56:20:INFO:Epoch: 1 global_step: 850/3750 lr: 0.00077 loss: 0.0023
2022-01-11 16:56:38:INFO:Epoch: 1 global_step: 900/3750 lr: 0.00076 loss: 0.0024
2022-01-11 16:56:57:INFO:Epoch: 1 global_step: 950/3750 lr: 0.00075 loss: 0.0022
2022-01-11 16:57:15:INFO:Epoch: 1 global_step: 1000/3750 lr: 0.00073 loss: 0.0023
2022-01-11 16:57:34:INFO:Epoch: 1 global_step: 1050/3750 lr: 0.00072 loss: 0.0025
2022-01-11 16:57:53:INFO:Epoch: 1 global_step: 1100/3750 lr: 0.00071 loss: 0.0021
2022-01-11 16:58:11:INFO:Epoch: 1 global_step: 1150/3750 lr: 0.00069 loss: 0.0019
2022-01-11 16:58:30:INFO:Epoch: 1 global_step: 1200/3750 lr: 0.00068 loss: 0.0020
2022-01-11 16:58:48:INFO:Epoch: 1 global_step: 1250/3750 lr: 0.00067 loss: 0.0021
2022-01-11 16:59:07:INFO:Epoch: 1 global_step: 1300/3750 lr: 0.00065 loss: 0.0019
2022-01-11 16:59:26:INFO:Epoch: 1 global_step: 1350/3750 lr: 0.00064 loss: 0.0020
2022-01-11 16:59:44:INFO:Epoch: 1 global_step: 1400/3750 lr: 0.00063 loss: 0.0019
2022-01-11 17:00:03:INFO:Epoch: 1 global_step: 1450/3750 lr: 0.00061 loss: 0.0020
2022-01-11 17:00:23:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 17:00:23:INFO: Num examples = 100
2022-01-11 17:00:23:INFO: RMSE = 32.0376
2022-01-11 17:00:26:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 17:00:26:INFO: Num examples = 100
2022-01-11 17:00:26:INFO: RMSE = 30.8539
2022-01-11 17:00:26:INFO:==> Minimal valid RMSE!
2022-01-11 17:00:26:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-128...
2022-01-11 17:00:26:INFO:Epoch: 2 global_step: 1500/3750 lr: 0.00060 loss: 0.0019
2022-01-11 17:00:45:INFO:Epoch: 2 global_step: 1550/3750 lr: 0.00059 loss: 0.0022
2022-01-11 17:01:03:INFO:Epoch: 2 global_step: 1600/3750 lr: 0.00057 loss: 0.0018
2022-01-11 17:01:22:INFO:Epoch: 2 global_step: 1650/3750 lr: 0.00056 loss: 0.0019
2022-01-11 17:01:41:INFO:Epoch: 2 global_step: 1700/3750 lr: 0.00055 loss: 0.0018
2022-01-11 17:01:59:INFO:Epoch: 2 global_step: 1750/3750 lr: 0.00053 loss: 0.0017
2022-01-11 17:02:18:INFO:Epoch: 2 global_step: 1800/3750 lr: 0.00052 loss: 0.0019
2022-01-11 17:02:36:INFO:Epoch: 2 global_step: 1850/3750 lr: 0.00051 loss: 0.0023
2022-01-11 17:02:55:INFO:Epoch: 2 global_step: 1900/3750 lr: 0.00049 loss: 0.0020
2022-01-11 17:03:14:INFO:Epoch: 2 global_step: 1950/3750 lr: 0.00048 loss: 0.0018
2022-01-11 17:03:32:INFO:Epoch: 2 global_step: 2000/3750 lr: 0.00047 loss: 0.0018
2022-01-11 17:03:51:INFO:Epoch: 2 global_step: 2050/3750 lr: 0.00045 loss: 0.0018
2022-01-11 17:04:09:INFO:Epoch: 2 global_step: 2100/3750 lr: 0.00044 loss: 0.0016
2022-01-11 17:04:28:INFO:Epoch: 2 global_step: 2150/3750 lr: 0.00043 loss: 0.0018
2022-01-11 17:04:47:INFO:Epoch: 2 global_step: 2200/3750 lr: 0.00041 loss: 0.0016
2022-01-11 17:05:07:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 17:05:07:INFO: Num examples = 100
2022-01-11 17:05:07:INFO: RMSE = 30.5710
2022-01-11 17:05:09:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 17:05:09:INFO: Num examples = 100
2022-01-11 17:05:09:INFO: RMSE = 33.1060
2022-01-11 17:05:10:INFO:Epoch: 3 global_step: 2250/3750 lr: 0.00040 loss: 0.0016
2022-01-11 17:05:28:INFO:Epoch: 3 global_step: 2300/3750 lr: 0.00039 loss: 0.0020
2022-01-11 17:05:46:INFO:Epoch: 3 global_step: 2350/3750 lr: 0.00037 loss: 0.0015
2022-01-11 17:06:05:INFO:Epoch: 3 global_step: 2400/3750 lr: 0.00036 loss: 0.0017
2022-01-11 17:06:23:INFO:Epoch: 3 global_step: 2450/3750 lr: 0.00035 loss: 0.0016
2022-01-11 17:06:42:INFO:Epoch: 3 global_step: 2500/3750 lr: 0.00033 loss: 0.0016
2022-01-11 17:07:01:INFO:Epoch: 3 global_step: 2550/3750 lr: 0.00032 loss: 0.0017
2022-01-11 17:07:19:INFO:Epoch: 3 global_step: 2600/3750 lr: 0.00031 loss: 0.0016
2022-01-11 17:07:38:INFO:Epoch: 3 global_step: 2650/3750 lr: 0.00029 loss: 0.0016
2022-01-11 17:07:56:INFO:Epoch: 3 global_step: 2700/3750 lr: 0.00028 loss: 0.0017
2022-01-11 17:08:15:INFO:Epoch: 3 global_step: 2750/3750 lr: 0.00027 loss: 0.0015
2022-01-11 17:08:34:INFO:Epoch: 3 global_step: 2800/3750 lr: 0.00025 loss: 0.0015
2022-01-11 17:08:52:INFO:Epoch: 3 global_step: 2850/3750 lr: 0.00024 loss: 0.0016
2022-01-11 17:09:11:INFO:Epoch: 3 global_step: 2900/3750 lr: 0.00023 loss: 0.0015
2022-01-11 17:09:29:INFO:Epoch: 3 global_step: 2950/3750 lr: 0.00021 loss: 0.0015
2022-01-11 17:09:50:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 17:09:50:INFO: Num examples = 100
2022-01-11 17:09:50:INFO: RMSE = 31.4520
2022-01-11 17:09:52:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 17:09:52:INFO: Num examples = 100
2022-01-11 17:09:52:INFO: RMSE = 33.0422
2022-01-11 17:09:52:INFO:Epoch: 4 global_step: 3000/3750 lr: 0.00020 loss: 0.0015
2022-01-11 17:10:11:INFO:Epoch: 4 global_step: 3050/3750 lr: 0.00019 loss: 0.0015
2022-01-11 17:10:29:INFO:Epoch: 4 global_step: 3100/3750 lr: 0.00017 loss: 0.0014
2022-01-11 17:10:48:INFO:Epoch: 4 global_step: 3150/3750 lr: 0.00016 loss: 0.0015
2022-01-11 17:11:06:INFO:Epoch: 4 global_step: 3200/3750 lr: 0.00015 loss: 0.0014
2022-01-11 17:11:25:INFO:Epoch: 4 global_step: 3250/3750 lr: 0.00013 loss: 0.0014
2022-01-11 17:11:43:INFO:Epoch: 4 global_step: 3300/3750 lr: 0.00012 loss: 0.0015
2022-01-11 17:12:02:INFO:Epoch: 4 global_step: 3350/3750 lr: 0.00011 loss: 0.0014
2022-01-11 17:12:20:INFO:Epoch: 4 global_step: 3400/3750 lr: 0.00009 loss: 0.0016
2022-01-11 17:12:39:INFO:Epoch: 4 global_step: 3450/3750 lr: 0.00008 loss: 0.0015
2022-01-11 17:12:58:INFO:Epoch: 4 global_step: 3500/3750 lr: 0.00007 loss: 0.0014
2022-01-11 17:13:16:INFO:Epoch: 4 global_step: 3550/3750 lr: 0.00005 loss: 0.0014
2022-01-11 17:13:35:INFO:Epoch: 4 global_step: 3600/3750 lr: 0.00004 loss: 0.0015
2022-01-11 17:13:53:INFO:Epoch: 4 global_step: 3650/3750 lr: 0.00003 loss: 0.0014
2022-01-11 17:14:12:INFO:Epoch: 4 global_step: 3700/3750 lr: 0.00001 loss: 0.0015
2022-01-11 17:14:32:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 17:14:32:INFO: Num examples = 100
2022-01-11 17:14:32:INFO: RMSE = 31.4752
2022-01-11 17:14:34:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 17:14:34:INFO: Num examples = 100
2022-01-11 17:14:34:INFO: RMSE = 30.4413
2022-01-11 17:14:34:INFO:==> Minimal valid RMSE!
2022-01-11 17:14:34:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-128...
2022-01-11 17:14:34:INFO: Output TEST RMSE: 31.4752
2022-01-11 17:14:34:INFO: VALID RMSEs: 31.5513 30.8539 33.1060 33.0422 30.4413
2022-01-11 17:14:34:INFO: TEST RMSEs: 31.0949 32.0376 30.5710 31.4520 31.4752
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
2022-01-11 17:14:37:INFO:Finish setting logger...
2022-01-11 17:14:37:INFO:==> Training/Evaluation parameters are:
2022-01-11 17:14:37:INFO: Namespace(model_dir='cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42'
2022-01-11 17:14:37:INFO: data_fn=1
2022-01-11 17:14:37:INFO: datatest_fn=1
2022-01-11 17:14:37:INFO: filter_kernel_size=1
2022-01-11 17:14:37:INFO: override_data_cache=False
2022-01-11 17:14:37:INFO: maxRUL=125
2022-01-11 17:14:37:INFO: low_ratio=0.1
2022-01-11 17:14:37:INFO: high_ratio=0.99
2022-01-11 17:14:37:INFO: aug_ratio=150
2022-01-11 17:14:37:INFO: noise_amplitude=0.01
2022-01-11 17:14:37:INFO: modeltype='cnn1d'
2022-01-11 17:14:37:INFO: max_seq_len=550
2022-01-11 17:14:37:INFO: d_model=128
2022-01-11 17:14:37:INFO: p_dropout=0.1
2022-01-11 17:14:37:INFO: n_head=4
2022-01-11 17:14:37:INFO: n_layer=2
2022-01-11 17:14:37:INFO: dim_feedforward=512
2022-01-11 17:14:37:INFO: e_dropout=0.1
2022-01-11 17:14:37:INFO: activation='relu'
2022-01-11 17:14:37:INFO: layer_norm=False
2022-01-11 17:14:37:INFO: support_size=2
2022-01-11 17:14:37:INFO: inner_steps=2
2022-01-11 17:14:37:INFO: lr_inner=0.0001
2022-01-11 17:14:37:INFO: lr_meta=0.001
2022-01-11 17:14:37:INFO: n_epochs=5
2022-01-11 17:14:37:INFO: train_batch_size=20
2022-01-11 17:14:37:INFO: eval_batch_size=1
2022-01-11 17:14:37:INFO: lr=0.001
2022-01-11 17:14:37:INFO: weight_decay=0.01
2022-01-11 17:14:37:INFO: warmup_ratio=0.0
2022-01-11 17:14:37:INFO: max_grad_norm=5.0
2022-01-11 17:14:37:INFO: logging_steps=50
2022-01-11 17:14:37:INFO: seed=42
2022-01-11 17:14:37:INFO: gpu_id=3
2022-01-11 17:14:37:INFO: do_train=True
2022-01-11 17:14:37:INFO: do_eval=False
2022-01-11 17:14:37:INFO: train_data_fn='data/train_FD001.txt'
2022-01-11 17:14:37:INFO: test_data_fn='data/test_FD001.txt'
2022-01-11 17:14:37:INFO: target_ruls_fn='data/RUL_FD001.txt'
2022-01-11 17:14:37:INFO: device=device(type='cuda'))
2022-01-11 17:14:37:INFO:Dump arguments to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 17:14:37:INFO:==> Read data from data/train_FD001.txt...
2022-01-11 17:14:37:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 17:14:38:INFO:==> Min_max normalization...
2022-01-11 17:14:38:INFO: The min value is [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 17:14:38:INFO: The max value is [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 17:14:38:INFO:==> Read data from data/test_FD001.txt...
2022-01-11 17:14:38:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 17:14:38:INFO:==> Read RULsfrom data/RUL_FD001.txt...
2022-01-11 17:14:38:INFO: min_rul: 7, max_rul: 145
2022-01-11 17:14:38:INFO:==> Input length ratio of the [TEST] data:
2022-01-11 17:14:38:INFO: min_ratio = 0.2067
2022-01-11 17:14:38:INFO: max_ratio = 0.9667
2022-01-11 17:14:38:INFO:==> Min_max normalization...
2022-01-11 17:14:38:INFO: With given min value [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 17:14:38:INFO: With given max value [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 17:14:38:INFO:==> Computing Criterion...
2022-01-11 17:14:38:INFO: The weights are: 0.007887763902544975, 0.008001004345715046, 0.06667434424161911, 0.0634712353348732, 0.07656104862689972, 0.0755249634385109, 0.06726357340812683, 0.0644979178905487, 0.0795108750462532, 0.07743842899799347, 0.0671684592962265, 0.06869389116764069, 0.07147877663373947, 0.06516212970018387, 0.07012488692998886, 0.07054071873426437
2022-01-11 17:14:45:INFO:Note: NumExpr detected 40 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
2022-01-11 17:14:45:INFO:NumExpr defaulting to 8 threads.
2022-01-11 17:14:45:INFO:=============== Scheme: Meta Learning ===============
2022-01-11 17:14:45:INFO: Num examples = 15000
2022-01-11 17:14:45:INFO: Num epochs = 5
2022-01-11 17:14:45:INFO: Batch size = 20
2022-01-11 17:14:45:INFO: Total meta optimization steps = 3750
2022-01-11 17:14:45:INFO: Total inner optimization steps = 7500
2022-01-11 17:14:54:INFO:==> Group parameters for optimization...
2022-01-11 17:14:54:INFO: Parameters to update are:
2022-01-11 17:14:54:INFO: conv1.0.weight
2022-01-11 17:14:54:INFO: conv2.0.weight
2022-01-11 17:14:54:INFO: conv3.0.weight
2022-01-11 17:14:54:INFO: conv4.0.weight
2022-01-11 17:14:54:INFO: conv5.0.weight
2022-01-11 17:14:54:INFO: fc_1.0.weight
2022-01-11 17:14:54:INFO: fc_1.0.bias
2022-01-11 17:14:54:INFO: fc_2.weight
2022-01-11 17:14:54:INFO: fc_2.bias
/data/moy20/Meta-Learning/Meta-prognosis-main/optimizer.py:78: UserWarning: This overload of add_ is deprecated:
add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other, *, Number alpha) (Triggered internally at /opt/conda/conda-bld/pytorch_1623448255797/work/torch/csrc/utils/python_arg_parser.cpp:1025.)
exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
/data/moy20/miniconda3/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:247: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
warnings.warn("To get the last learning rate computed by the scheduler, "
2022-01-11 17:14:57:INFO:Epoch: 0 global_step: 0/3750 lr: 0.00100 loss: 0.0011
2022-01-11 17:16:11:INFO:Epoch: 0 global_step: 50/3750 lr: 0.00099 loss: 0.0166
2022-01-11 17:17:26:INFO:Epoch: 0 global_step: 100/3750 lr: 0.00097 loss: 0.0067
2022-01-11 17:18:40:INFO:Epoch: 0 global_step: 150/3750 lr: 0.00096 loss: 0.0061
2022-01-11 17:19:55:INFO:Epoch: 0 global_step: 200/3750 lr: 0.00095 loss: 0.0064
2022-01-11 17:21:11:INFO:Epoch: 0 global_step: 250/3750 lr: 0.00093 loss: 0.0051
2022-01-11 17:22:26:INFO:Epoch: 0 global_step: 300/3750 lr: 0.00092 loss: 0.0039
2022-01-11 17:23:41:INFO:Epoch: 0 global_step: 350/3750 lr: 0.00091 loss: 0.0038
2022-01-11 17:24:56:INFO:Epoch: 0 global_step: 400/3750 lr: 0.00089 loss: 0.0030
2022-01-11 17:26:11:INFO:Epoch: 0 global_step: 450/3750 lr: 0.00088 loss: 0.0028
2022-01-11 17:27:26:INFO:Epoch: 0 global_step: 500/3750 lr: 0.00087 loss: 0.0024
2022-01-11 17:28:41:INFO:Epoch: 0 global_step: 550/3750 lr: 0.00085 loss: 0.0025
2022-01-11 17:29:56:INFO:Epoch: 0 global_step: 600/3750 lr: 0.00084 loss: 0.0024
2022-01-11 17:31:11:INFO:Epoch: 0 global_step: 650/3750 lr: 0.00083 loss: 0.0023
2022-01-11 17:32:26:INFO:Epoch: 0 global_step: 700/3750 lr: 0.00081 loss: 0.0019
2022-01-11 17:33:47:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 17:33:47:INFO: Num examples = 100
2022-01-11 17:33:47:INFO: RMSE = 32.5962
2022-01-11 17:33:54:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 17:33:54:INFO: Num examples = 100
2022-01-11 17:33:54:INFO: RMSE = 26.1790
2022-01-11 17:33:54:INFO:==> Minimal valid RMSE!
2022-01-11 17:33:54:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 17:33:56:INFO:Epoch: 1 global_step: 750/3750 lr: 0.00080 loss: 0.0017
2022-01-11 17:35:11:INFO:Epoch: 1 global_step: 800/3750 lr: 0.00079 loss: 0.0019
2022-01-11 17:36:26:INFO:Epoch: 1 global_step: 850/3750 lr: 0.00077 loss: 0.0016
2022-01-11 17:37:41:INFO:Epoch: 1 global_step: 900/3750 lr: 0.00076 loss: 0.0015
2022-01-11 17:38:55:INFO:Epoch: 1 global_step: 950/3750 lr: 0.00075 loss: 0.0016
2022-01-11 17:40:10:INFO:Epoch: 1 global_step: 1000/3750 lr: 0.00073 loss: 0.0014
2022-01-11 17:41:25:INFO:Epoch: 1 global_step: 1050/3750 lr: 0.00072 loss: 0.0014
2022-01-11 17:42:39:INFO:Epoch: 1 global_step: 1100/3750 lr: 0.00071 loss: 0.0015
2022-01-11 17:43:54:INFO:Epoch: 1 global_step: 1150/3750 lr: 0.00069 loss: 0.0013
2022-01-11 17:45:09:INFO:Epoch: 1 global_step: 1200/3750 lr: 0.00068 loss: 0.0012
2022-01-11 17:46:24:INFO:Epoch: 1 global_step: 1250/3750 lr: 0.00067 loss: 0.0013
2022-01-11 17:47:39:INFO:Epoch: 1 global_step: 1300/3750 lr: 0.00065 loss: 0.0013
2022-01-11 17:48:53:INFO:Epoch: 1 global_step: 1350/3750 lr: 0.00064 loss: 0.0012
2022-01-11 17:50:08:INFO:Epoch: 1 global_step: 1400/3750 lr: 0.00063 loss: 0.0012
2022-01-11 17:51:23:INFO:Epoch: 1 global_step: 1450/3750 lr: 0.00061 loss: 0.0013
2022-01-11 17:52:43:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 17:52:43:INFO: Num examples = 100
2022-01-11 17:52:43:INFO: RMSE = 33.7692
2022-01-11 17:52:51:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 17:52:51:INFO: Num examples = 100
2022-01-11 17:52:51:INFO: RMSE = 22.0666
2022-01-11 17:52:51:INFO:==> Minimal valid RMSE!
2022-01-11 17:52:51:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 17:52:52:INFO:Epoch: 2 global_step: 1500/3750 lr: 0.00060 loss: 0.0011
2022-01-11 17:54:07:INFO:Epoch: 2 global_step: 1550/3750 lr: 0.00059 loss: 0.0011
2022-01-11 17:55:22:INFO:Epoch: 2 global_step: 1600/3750 lr: 0.00057 loss: 0.0011
2022-01-11 17:56:37:INFO:Epoch: 2 global_step: 1650/3750 lr: 0.00056 loss: 0.0011
2022-01-11 17:57:52:INFO:Epoch: 2 global_step: 1700/3750 lr: 0.00055 loss: 0.0011
2022-01-11 17:59:06:INFO:Epoch: 2 global_step: 1750/3750 lr: 0.00053 loss: 0.0011
2022-01-11 18:00:20:INFO:Epoch: 2 global_step: 1800/3750 lr: 0.00052 loss: 0.0011
2022-01-11 18:01:35:INFO:Epoch: 2 global_step: 1850/3750 lr: 0.00051 loss: 0.0010
2022-01-11 18:02:49:INFO:Epoch: 2 global_step: 1900/3750 lr: 0.00049 loss: 0.0011
2022-01-11 18:04:04:INFO:Epoch: 2 global_step: 1950/3750 lr: 0.00048 loss: 0.0010
2022-01-11 18:05:19:INFO:Epoch: 2 global_step: 2000/3750 lr: 0.00047 loss: 0.0009
2022-01-11 18:06:34:INFO:Epoch: 2 global_step: 2050/3750 lr: 0.00045 loss: 0.0010
2022-01-11 18:07:48:INFO:Epoch: 2 global_step: 2100/3750 lr: 0.00044 loss: 0.0010
2022-01-11 18:09:02:INFO:Epoch: 2 global_step: 2150/3750 lr: 0.00043 loss: 0.0009
2022-01-11 18:10:16:INFO:Epoch: 2 global_step: 2200/3750 lr: 0.00041 loss: 0.0010
2022-01-11 18:11:38:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 18:11:38:INFO: Num examples = 100
2022-01-11 18:11:38:INFO: RMSE = 32.2663
2022-01-11 18:11:45:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 18:11:45:INFO: Num examples = 100
2022-01-11 18:11:45:INFO: RMSE = 21.7572
2022-01-11 18:11:45:INFO:==> Minimal valid RMSE!
2022-01-11 18:11:45:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 18:11:46:INFO:Epoch: 3 global_step: 2250/3750 lr: 0.00040 loss: 0.0009
2022-01-11 18:13:01:INFO:Epoch: 3 global_step: 2300/3750 lr: 0.00039 loss: 0.0009
2022-01-11 18:14:15:INFO:Epoch: 3 global_step: 2350/3750 lr: 0.00037 loss: 0.0009
2022-01-11 18:15:30:INFO:Epoch: 3 global_step: 2400/3750 lr: 0.00036 loss: 0.0008
2022-01-11 18:16:44:INFO:Epoch: 3 global_step: 2450/3750 lr: 0.00035 loss: 0.0008
2022-01-11 18:17:57:INFO:Epoch: 3 global_step: 2500/3750 lr: 0.00033 loss: 0.0007
2022-01-11 18:19:12:INFO:Epoch: 3 global_step: 2550/3750 lr: 0.00032 loss: 0.0009
2022-01-11 18:20:26:INFO:Epoch: 3 global_step: 2600/3750 lr: 0.00031 loss: 0.0008
2022-01-11 18:21:41:INFO:Epoch: 3 global_step: 2650/3750 lr: 0.00029 loss: 0.0008
2022-01-11 18:22:55:INFO:Epoch: 3 global_step: 2700/3750 lr: 0.00028 loss: 0.0007
2022-01-11 18:24:10:INFO:Epoch: 3 global_step: 2750/3750 lr: 0.00027 loss: 0.0009
2022-01-11 18:25:25:INFO:Epoch: 3 global_step: 2800/3750 lr: 0.00025 loss: 0.0007
2022-01-11 18:26:40:INFO:Epoch: 3 global_step: 2850/3750 lr: 0.00024 loss: 0.0007
2022-01-11 18:27:54:INFO:Epoch: 3 global_step: 2900/3750 lr: 0.00023 loss: 0.0007
2022-01-11 18:29:10:INFO:Epoch: 3 global_step: 2950/3750 lr: 0.00021 loss: 0.0007
2022-01-11 18:30:30:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 18:30:30:INFO: Num examples = 100
2022-01-11 18:30:30:INFO: RMSE = 33.1717
2022-01-11 18:30:37:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 18:30:37:INFO: Num examples = 100
2022-01-11 18:30:37:INFO: RMSE = 21.7469
2022-01-11 18:30:37:INFO:==> Minimal valid RMSE!
2022-01-11 18:30:37:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 18:30:38:INFO:Epoch: 4 global_step: 3000/3750 lr: 0.00020 loss: 0.0007
2022-01-11 18:31:53:INFO:Epoch: 4 global_step: 3050/3750 lr: 0.00019 loss: 0.0006
2022-01-11 18:33:08:INFO:Epoch: 4 global_step: 3100/3750 lr: 0.00017 loss: 0.0006
2022-01-11 18:34:22:INFO:Epoch: 4 global_step: 3150/3750 lr: 0.00016 loss: 0.0007
2022-01-11 18:35:37:INFO:Epoch: 4 global_step: 3200/3750 lr: 0.00015 loss: 0.0007
2022-01-11 18:36:51:INFO:Epoch: 4 global_step: 3250/3750 lr: 0.00013 loss: 0.0007
2022-01-11 18:38:06:INFO:Epoch: 4 global_step: 3300/3750 lr: 0.00012 loss: 0.0006
2022-01-11 18:39:20:INFO:Epoch: 4 global_step: 3350/3750 lr: 0.00011 loss: 0.0006
2022-01-11 18:40:35:INFO:Epoch: 4 global_step: 3400/3750 lr: 0.00009 loss: 0.0007
2022-01-11 18:41:50:INFO:Epoch: 4 global_step: 3450/3750 lr: 0.00008 loss: 0.0007
2022-01-11 18:43:05:INFO:Epoch: 4 global_step: 3500/3750 lr: 0.00007 loss: 0.0006
2022-01-11 18:44:20:INFO:Epoch: 4 global_step: 3550/3750 lr: 0.00005 loss: 0.0007
2022-01-11 18:45:35:INFO:Epoch: 4 global_step: 3600/3750 lr: 0.00004 loss: 0.0006
2022-01-11 18:46:49:INFO:Epoch: 4 global_step: 3650/3750 lr: 0.00003 loss: 0.0007
2022-01-11 18:48:04:INFO:Epoch: 4 global_step: 3700/3750 lr: 0.00001 loss: 0.0006
2022-01-11 18:49:24:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 18:49:24:INFO: Num examples = 100
2022-01-11 18:49:24:INFO: RMSE = 32.4648
2022-01-11 18:49:31:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 18:49:31:INFO: Num examples = 100
2022-01-11 18:49:31:INFO: RMSE = 23.5723
2022-01-11 18:49:31:INFO: Output TEST RMSE: 33.1717
2022-01-11 18:49:31:INFO: VALID RMSEs: 26.1790 22.0666 21.7572 21.7469 23.5723
2022-01-11 18:49:31:INFO: TEST RMSEs: 32.5962 33.7692 32.2663 33.1717 32.4648
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
2022-01-11 18:49:35:INFO:Finish setting logger...
2022-01-11 18:49:35:INFO:==> Training/Evaluation parameters are:
2022-01-11 18:49:35:INFO: Namespace(model_dir='cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.001_warmUp-0.0_seed-42'
2022-01-11 18:49:35:INFO: data_fn=1
2022-01-11 18:49:35:INFO: datatest_fn=1
2022-01-11 18:49:35:INFO: filter_kernel_size=1
2022-01-11 18:49:35:INFO: override_data_cache=False
2022-01-11 18:49:35:INFO: maxRUL=125
2022-01-11 18:49:35:INFO: low_ratio=0.1
2022-01-11 18:49:35:INFO: high_ratio=0.99
2022-01-11 18:49:35:INFO: aug_ratio=150
2022-01-11 18:49:35:INFO: noise_amplitude=0.01
2022-01-11 18:49:35:INFO: modeltype='cnn1d'
2022-01-11 18:49:35:INFO: max_seq_len=550
2022-01-11 18:49:35:INFO: d_model=128
2022-01-11 18:49:35:INFO: p_dropout=0.1
2022-01-11 18:49:35:INFO: n_head=4
2022-01-11 18:49:35:INFO: n_layer=2
2022-01-11 18:49:35:INFO: dim_feedforward=512
2022-01-11 18:49:35:INFO: e_dropout=0.1
2022-01-11 18:49:35:INFO: activation='relu'
2022-01-11 18:49:35:INFO: layer_norm=False
2022-01-11 18:49:35:INFO: support_size=2
2022-01-11 18:49:35:INFO: inner_steps=2
2022-01-11 18:49:35:INFO: lr_inner=0.001
2022-01-11 18:49:35:INFO: lr_meta=0.001
2022-01-11 18:49:35:INFO: n_epochs=5
2022-01-11 18:49:35:INFO: train_batch_size=20
2022-01-11 18:49:35:INFO: eval_batch_size=1
2022-01-11 18:49:35:INFO: lr=0.001
2022-01-11 18:49:35:INFO: weight_decay=0.01
2022-01-11 18:49:35:INFO: warmup_ratio=0.0
2022-01-11 18:49:35:INFO: max_grad_norm=5.0
2022-01-11 18:49:35:INFO: logging_steps=50
2022-01-11 18:49:35:INFO: seed=42
2022-01-11 18:49:35:INFO: gpu_id=3
2022-01-11 18:49:35:INFO: do_train=True
2022-01-11 18:49:35:INFO: do_eval=False
2022-01-11 18:49:35:INFO: train_data_fn='data/train_FD001.txt'
2022-01-11 18:49:35:INFO: test_data_fn='data/test_FD001.txt'
2022-01-11 18:49:35:INFO: target_ruls_fn='data/RUL_FD001.txt'
2022-01-11 18:49:35:INFO: device=device(type='cuda'))
2022-01-11 18:49:35:INFO:Dump arguments to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.001_warmUp-0.0_seed-42...
2022-01-11 18:49:35:INFO:==> Read data from data/train_FD001.txt...
2022-01-11 18:49:35:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 18:49:35:INFO:==> Min_max normalization...
2022-01-11 18:49:35:INFO: The min value is [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 18:49:35:INFO: The max value is [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 18:49:35:INFO:==> Read data from data/test_FD001.txt...
2022-01-11 18:49:35:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 18:49:35:INFO:==> Read RULsfrom data/RUL_FD001.txt...
2022-01-11 18:49:35:INFO: min_rul: 7, max_rul: 145
2022-01-11 18:49:35:INFO:==> Input length ratio of the [TEST] data:
2022-01-11 18:49:35:INFO: min_ratio = 0.2067
2022-01-11 18:49:35:INFO: max_ratio = 0.9667
2022-01-11 18:49:35:INFO:==> Min_max normalization...
2022-01-11 18:49:35:INFO: With given min value [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 18:49:35:INFO: With given max value [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 18:49:35:INFO:==> Computing Criterion...
2022-01-11 18:49:35:INFO: The weights are: 0.007887763902544975, 0.008001004345715046, 0.06667434424161911, 0.0634712353348732, 0.07656104862689972, 0.0755249634385109, 0.06726357340812683, 0.0644979178905487, 0.0795108750462532, 0.07743842899799347, 0.0671684592962265, 0.06869389116764069, 0.07147877663373947, 0.06516212970018387, 0.07012488692998886, 0.07054071873426437
2022-01-11 18:49:43:INFO:Note: NumExpr detected 40 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
2022-01-11 18:49:43:INFO:NumExpr defaulting to 8 threads.
2022-01-11 18:49:43:INFO:=============== Scheme: Meta Learning ===============
2022-01-11 18:49:43:INFO: Num examples = 15000
2022-01-11 18:49:43:INFO: Num epochs = 5
2022-01-11 18:49:43:INFO: Batch size = 20
2022-01-11 18:49:43:INFO: Total meta optimization steps = 3750
2022-01-11 18:49:43:INFO: Total inner optimization steps = 7500
2022-01-11 18:49:51:INFO:==> Group parameters for optimization...
2022-01-11 18:49:51:INFO: Parameters to update are:
2022-01-11 18:49:51:INFO: conv1.0.weight
2022-01-11 18:49:51:INFO: conv2.0.weight
2022-01-11 18:49:51:INFO: conv3.0.weight
2022-01-11 18:49:51:INFO: conv4.0.weight
2022-01-11 18:49:51:INFO: conv5.0.weight
2022-01-11 18:49:51:INFO: fc_1.0.weight
2022-01-11 18:49:51:INFO: fc_1.0.bias
2022-01-11 18:49:51:INFO: fc_2.weight
2022-01-11 18:49:51:INFO: fc_2.bias
/data/moy20/Meta-Learning/Meta-prognosis-main/optimizer.py:78: UserWarning: This overload of add_ is deprecated:
add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other, *, Number alpha) (Triggered internally at /opt/conda/conda-bld/pytorch_1623448255797/work/torch/csrc/utils/python_arg_parser.cpp:1025.)
exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
/data/moy20/miniconda3/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:247: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
warnings.warn("To get the last learning rate computed by the scheduler, "
2022-01-11 18:49:54:INFO:Epoch: 0 global_step: 0/3750 lr: 0.00100 loss: 0.0011
2022-01-11 18:51:08:INFO:Epoch: 0 global_step: 50/3750 lr: 0.00099 loss: 0.0166
2022-01-11 18:52:23:INFO:Epoch: 0 global_step: 100/3750 lr: 0.00097 loss: 0.0067
2022-01-11 18:53:37:INFO:Epoch: 0 global_step: 150/3750 lr: 0.00096 loss: 0.0061
2022-01-11 18:54:52:INFO:Epoch: 0 global_step: 200/3750 lr: 0.00095 loss: 0.0064
2022-01-11 18:56:06:INFO:Epoch: 0 global_step: 250/3750 lr: 0.00093 loss: 0.0051
2022-01-11 18:57:22:INFO:Epoch: 0 global_step: 300/3750 lr: 0.00092 loss: 0.0039
2022-01-11 18:58:36:INFO:Epoch: 0 global_step: 350/3750 lr: 0.00091 loss: 0.0038
2022-01-11 18:59:52:INFO:Epoch: 0 global_step: 400/3750 lr: 0.00089 loss: 0.0030
2022-01-11 19:01:06:INFO:Epoch: 0 global_step: 450/3750 lr: 0.00088 loss: 0.0028
2022-01-11 19:02:21:INFO:Epoch: 0 global_step: 500/3750 lr: 0.00087 loss: 0.0024
2022-01-11 19:03:35:INFO:Epoch: 0 global_step: 550/3750 lr: 0.00085 loss: 0.0025
2022-01-11 19:04:49:INFO:Epoch: 0 global_step: 600/3750 lr: 0.00084 loss: 0.0024
2022-01-11 19:06:03:INFO:Epoch: 0 global_step: 650/3750 lr: 0.00083 loss: 0.0023
2022-01-11 19:07:18:INFO:Epoch: 0 global_step: 700/3750 lr: 0.00081 loss: 0.0019
2022-01-11 19:08:38:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 19:08:38:INFO: Num examples = 100
2022-01-11 19:08:38:INFO: RMSE = 32.5962
2022-01-11 19:08:45:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 19:08:45:INFO: Num examples = 100
2022-01-11 19:08:45:INFO: RMSE = 26.1790
2022-01-11 19:08:45:INFO:==> Minimal valid RMSE!
2022-01-11 19:08:45:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.001_warmUp-0.0_seed-42...
2022-01-11 19:08:47:INFO:Epoch: 1 global_step: 750/3750 lr: 0.00080 loss: 0.0017
2022-01-11 19:10:02:INFO:Epoch: 1 global_step: 800/3750 lr: 0.00079 loss: 0.0019
2022-01-11 19:11:16:INFO:Epoch: 1 global_step: 850/3750 lr: 0.00077 loss: 0.0016
2022-01-11 19:12:31:INFO:Epoch: 1 global_step: 900/3750 lr: 0.00076 loss: 0.0015
2022-01-11 19:13:45:INFO:Epoch: 1 global_step: 950/3750 lr: 0.00075 loss: 0.0016
2022-01-11 19:15:00:INFO:Epoch: 1 global_step: 1000/3750 lr: 0.00073 loss: 0.0014
2022-01-11 19:16:14:INFO:Epoch: 1 global_step: 1050/3750 lr: 0.00072 loss: 0.0014
2022-01-11 19:17:29:INFO:Epoch: 1 global_step: 1100/3750 lr: 0.00071 loss: 0.0015
2022-01-11 19:18:44:INFO:Epoch: 1 global_step: 1150/3750 lr: 0.00069 loss: 0.0013
2022-01-11 19:19:59:INFO:Epoch: 1 global_step: 1200/3750 lr: 0.00068 loss: 0.0012
2022-01-11 19:21:14:INFO:Epoch: 1 global_step: 1250/3750 lr: 0.00067 loss: 0.0013
2022-01-11 19:22:29:INFO:Epoch: 1 global_step: 1300/3750 lr: 0.00065 loss: 0.0013
2022-01-11 19:23:44:INFO:Epoch: 1 global_step: 1350/3750 lr: 0.00064 loss: 0.0012
2022-01-11 19:25:00:INFO:Epoch: 1 global_step: 1400/3750 lr: 0.00063 loss: 0.0012
2022-01-11 19:26:14:INFO:Epoch: 1 global_step: 1450/3750 lr: 0.00061 loss: 0.0013
2022-01-11 19:27:35:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 19:27:35:INFO: Num examples = 100
2022-01-11 19:27:35:INFO: RMSE = 33.7692
2022-01-11 19:27:42:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 19:27:42:INFO: Num examples = 100
2022-01-11 19:27:42:INFO: RMSE = 22.0666
2022-01-11 19:27:42:INFO:==> Minimal valid RMSE!
2022-01-11 19:27:42:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.001_warmUp-0.0_seed-42...
2022-01-11 19:27:43:INFO:Epoch: 2 global_step: 1500/3750 lr: 0.00060 loss: 0.0011
2022-01-11 19:28:58:INFO:Epoch: 2 global_step: 1550/3750 lr: 0.00059 loss: 0.0011
2022-01-11 19:30:12:INFO:Epoch: 2 global_step: 1600/3750 lr: 0.00057 loss: 0.0011
2022-01-11 19:31:25:INFO:Epoch: 2 global_step: 1650/3750 lr: 0.00056 loss: 0.0011
2022-01-11 19:32:38:INFO:Epoch: 2 global_step: 1700/3750 lr: 0.00055 loss: 0.0011
2022-01-11 19:33:52:INFO:Epoch: 2 global_step: 1750/3750 lr: 0.00053 loss: 0.0011
2022-01-11 19:35:06:INFO:Epoch: 2 global_step: 1800/3750 lr: 0.00052 loss: 0.0011
2022-01-11 19:36:19:INFO:Epoch: 2 global_step: 1850/3750 lr: 0.00051 loss: 0.0010
2022-01-11 19:37:33:INFO:Epoch: 2 global_step: 1900/3750 lr: 0.00049 loss: 0.0011
2022-01-11 19:38:47:INFO:Epoch: 2 global_step: 1950/3750 lr: 0.00048 loss: 0.0010
2022-01-11 19:40:00:INFO:Epoch: 2 global_step: 2000/3750 lr: 0.00047 loss: 0.0009
2022-01-11 19:41:14:INFO:Epoch: 2 global_step: 2050/3750 lr: 0.00045 loss: 0.0010
2022-01-11 19:42:28:INFO:Epoch: 2 global_step: 2100/3750 lr: 0.00044 loss: 0.0010
2022-01-11 19:43:41:INFO:Epoch: 2 global_step: 2150/3750 lr: 0.00043 loss: 0.0009
2022-01-11 19:44:54:INFO:Epoch: 2 global_step: 2200/3750 lr: 0.00041 loss: 0.0010
2022-01-11 19:46:14:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 19:46:14:INFO: Num examples = 100
2022-01-11 19:46:14:INFO: RMSE = 32.2663
2022-01-11 19:46:22:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 19:46:22:INFO: Num examples = 100
2022-01-11 19:46:22:INFO: RMSE = 21.7572
2022-01-11 19:46:22:INFO:==> Minimal valid RMSE!
2022-01-11 19:46:22:INFO:Save model to cnn1dmodels/data-1_n_epochs-5_aug-150_noise-0.01_supportSize-2_innerSteps-2_lrMeta-0.001_lrInner-0.001_warmUp-0.0_seed-42...
2022-01-11 19:46:23:INFO:Epoch: 3 global_step: 2250/3750 lr: 0.00040 loss: 0.0009
2022-01-11 19:47:38:INFO:Epoch: 3 global_step: 2300/3750 lr: 0.00039 loss: 0.0009
2022-01-11 19:48:53:INFO:Epoch: 3 global_step: 2350/3750 lr: 0.00037 loss: 0.0009
2022-01-11 19:50:08:INFO:Epoch: 3 global_step: 2400/3750 lr: 0.00036 loss: 0.0008
2022-01-11 19:51:22:INFO:Epoch: 3 global_step: 2450/3750 lr: 0.00035 loss: 0.0008