modified: checkpoints/forward/best_tcn_model.pt modified: checkpoints/forward/training_history.csv new file: checkpoints_mlp/task1_feature_mlp/best_feature_mlp.pt new file: checkpoints_mlp/task1_feature_mlp/training_history.csv new file: checkpoints_rms/forward_rms/best_rms_model.pt new file: checkpoints_rms/forward_rms/training_history.csv modified: evaluation_outputs/forward/evaluation_forward_test_b0_s0.png new file: evaluation_outputs/forward/evaluation_forward_val_b0_s0.png new file: evaluation_outputs/forward/evaluation_forward_val_b3_s0.png new file: evaluation_outputs/forward_rms/evaluation_test_all_samples.csv new file: evaluation_outputs/forward_rms/evaluation_test_s0.png new file: evaluation_outputs/forward_rms/evaluation_train_all_samples.csv new file: evaluation_outputs/forward_rms/evaluation_val_all_samples.csv new file: evaluation_outputs/forward_rms/evaluation_val_s0.png new file: evaluation_outputs/forward_rms/evaluation_val_s0_waveform.png new file: evaluation_outputs/task1_feature_mlp/evaluation_train_all_samples.csv new file: evaluation_outputs/task1_feature_mlp/evaluation_train_curve.png new file: evaluation_outputs/task1_feature_mlp/evaluation_val_all_samples.csv new file: evaluation_outputs/task1_feature_mlp/evaluation_val_curve.png new file: evaluation_outputs/task1_feature_mlp/evaluation_val_s0.png new file: evaluation_outputs/task1_feature_mlp/harmonic_5mm_0.75Hz_prediction.png new file: evaluation_outputs/task1_feature_mlp/harmonic_5mm_1.55Hz_prediction.png new file: scripts/__pycache__/config.cpython-310.pyc new file: scripts/__pycache__/dataset.cpython-310.pyc new file: scripts/__pycache__/model.cpython-310.pyc new file: scripts/config.py new file: scripts/dataset.py new file: scripts/evaluate.py new file: scripts/model.py new file: scripts/predict_single.py new file: scripts/train.py modified: src/__pycache__/config.cpython-310.pyc modified: src/__pycache__/dataset.cpython-310.pyc modified: src/__pycache__/model.cpython-310.pyc modified: src/config.py modified: src/dataset.py modified: src/model.py new file: src_new/__pycache__/config.cpython-310.pyc new file: src_new/__pycache__/dataset.cpython-310.pyc new file: src_new/__pycache__/evaluate.cpython-310.pyc new file: src_new/__pycache__/model.cpython-310.pyc new file: src_new/__pycache__/train.cpython-310.pyc new file: src_new/config.py new file: src_new/dataset.py new file: src_new/evaluate.py new file: src_new/model.py new file: src_new/train.py
4.2 KiB
4.2 KiB
| 1 | epoch | lr | train_loss | train_time_loss | train_weighted_time_loss | train_fft_loss | train_rms_loss | train_scale_loss | train_underestimate_loss | train_envelope_loss | train_rms_error | train_dominant_freq_error | val_loss | val_time_loss | val_weighted_time_loss | val_fft_loss | val_rms_loss | val_scale_loss | val_underestimate_loss | val_envelope_loss | val_rms_error | val_dominant_freq_error |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 1 | 0.001 | 25.599940884788083 | 1.7562742283884085 | 0.1756274257347269 | 2774.0617503040244 | 0.48780856886 | 0.4243854204157613 | 0.1559595560549565 | 0.8805500887474924 | 0.48780856886 | 0.5304876364710495 | 11.634879671294113 | 0.5565754904829222 | 0.05565755005026686 | 1030.2479747903758 | 0.19599251089424924 | 0.30123303316790484 | 0.07480601036262795 | 0.6266409343686598 | 0.19599251089424924 | 1.0759024783184585 |
| 3 | 2 | 0.001 | 18.8950198911271 | 1.807937761522689 | 0.18079377879511635 | 1975.3159283332104 | 0.3459213998801303 | 0.35067820830165214 | 0.1092575388907824 | 0.758364588865694 | 0.3459213998801303 | 0.4821035959354592 | 10.542049144876414 | 0.5095152341086289 | 0.05095152441283752 | 783.4861005585769 | 0.16758740950247336 | 0.2732687505154774 | 0.10411370940634916 | 0.6904992646184461 | 0.16758740950247336 | 0.9969777073497166 |
| 4 | 3 | 0.001 | 16.69826183678969 | 1.7823544706938401 | 0.17823545016207784 | 1672.4529568654186 | 0.30989001663226 | 0.3281119924108937 | 0.10324830568905147 | 0.7008864249823228 | 0.30989001663226 | 0.5562505104268722 | 11.536806665617844 | 0.5393576467859333 | 0.05393576609163449 | 807.3707762093379 | 0.21286767027501402 | 0.33425827504232014 | 0.10436723167718999 | 0.6995668801768072 | 0.21286767027501402 | 1.0535930765001758 |
| 5 | 4 | 0.001 | 14.293646308611024 | 1.7581631035174963 | 0.1758163128540201 | 1394.1521462494472 | 0.25787185048157313 | 0.2936001386282579 | 0.08524404234200153 | 0.6856944434485346 | 0.25787185048157313 | 0.4707189468993712 | 11.0088351677204 | 0.512058621850507 | 0.05120586244196727 | 716.754319289635 | 0.18768149871250678 | 0.30934940792363264 | 0.12457355485972145 | 0.7022169437901727 | 0.18768149871250678 | 0.9801404230908486 |
| 6 | 5 | 0.001 | 13.64289491581467 | 1.7736638503254585 | 0.1773663880127781 | 1282.9595152656987 | 0.2586157521549261 | 0.2966467724093851 | 0.0801986690856657 | 0.662726102291413 | 0.2586157521549261 | 0.4816929557024733 | 12.69674718791041 | 0.5321119869577473 | 0.0532112003400408 | 923.869591548525 | 0.2018765033832912 | 0.3501281591838804 | 0.1300795058286267 | 0.763439338782738 | 0.2018765033832912 | 0.8944807380729035 |
| 7 | 6 | 0.001 | 11.776648678869572 | 1.7715154886245728 | 0.17715155192703572 | 1060.149999654518 | 0.21744662122625225 | 0.2669158032480276 | 0.07291070573067046 | 0.6286382666736279 | 0.21744662122625225 | 0.49275163173347086 | 13.566723692006079 | 0.5599699513665561 | 0.055996995740409554 | 1081.8859031940328 | 0.23347977780062576 | 0.399347482827203 | 0.09571884981966738 | 0.753588840879243 | 0.23347977780062576 | 0.802507071705832 |
| 8 | 7 | 0.001 | 10.598831523139522 | 1.8021629632643934 | 0.180216298631902 | 959.8165948256006 | 0.1918528508746399 | 0.24521326632151064 | 0.06225078732197015 | 0.5634953882896675 | 0.1918528508746399 | 0.43498506576279067 | 12.335502788938324 | 0.5373578256574171 | 0.053735782938270735 | 897.6921434073613 | 0.22004679693230267 | 0.3851976631016567 | 0.09088767872288309 | 0.7485740184783936 | 0.22004679693230267 | 0.7111648032890617 |
| 9 | 8 | 0.0005 | 10.096835316352124 | 1.771101622086651 | 0.17711016507643573 | 904.1345197569649 | 0.18218446649470418 | 0.2265465495721349 | 0.06017633231427028 | 0.5831915904890816 | 0.18218446649470418 | 0.4688892364850107 | 12.481576097422632 | 0.55741053922423 | 0.05574105519416003 | 993.8351429906385 | 0.20113462980451255 | 0.37394588877414836 | 0.08785425563310754 | 0.7251736295634302 | 0.20113462980451255 | 0.7193729794563382 |
| 10 | 9 | 0.0005 | 9.301918011791301 | 1.8092092241881028 | 0.18092092534281173 | 872.4488234609928 | 0.15496070823579464 | 0.2050890047454609 | 0.05094481654078612 | 0.5316191723324218 | 0.15496070823579464 | 0.45952377956299667 | 12.33887189010094 | 0.529184412339638 | 0.052918442107480146 | 888.4320715542498 | 0.2059096264941939 | 0.35693827361382285 | 0.11748063429820768 | 0.7306599390917811 | 0.2059096264941939 | 0.7187415812949122 |
| 11 | 10 | 0.0005 | 9.509747361237148 | 1.775009681593697 | 0.17750097143481364 | 875.1525979671839 | 0.15211023376235422 | 0.21900151374767413 | 0.054768192924488826 | 0.5556492130711393 | 0.15211023376235422 | 0.48492007759302524 | 11.80303616359316 | 0.5109482181483301 | 0.05109482271404102 | 766.1427895118451 | 0.20308723608995305 | 0.33445684663180647 | 0.13568100256138835 | 0.7249893361124499 | 0.20308723608995305 | 0.7126380656820888 |