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
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| 1 | epoch | lr | train_loss | train_relative_loss | train_log_loss | train_mae_loss | train_waveform_l1 | train_waveform_huber | train_rms_error | val_loss | val_relative_loss | val_log_loss | val_mae_loss | val_waveform_l1 | val_waveform_huber | val_rms_error |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 1 | 0.001 | 0.3693518406814999 | 0.24889015323585933 | 0.05584108498361376 | 0.25861359967125785 | 0.5032549103101095 | 0.25580188632011414 | 0.24889015323585933 | 0.4390127956867218 | 0.30896639823913574 | 0.056898053735494614 | 0.3188031315803528 | 0.44043534994125366 | 0.17367032170295715 | 0.30896639823913574 |
| 3 | 2 | 0.001 | 0.3945225642787086 | 0.2697031597296397 | 0.06360895559191704 | 0.27172964480188155 | 0.4614827699131436 | 0.224760792321629 | 0.2697031597296397 | 0.36929574608802795 | 0.2602313160896301 | 0.0452926866710186 | 0.2584935128688812 | 0.4390406906604767 | 0.17246951162815094 | 0.2602313160896301 |
| 4 | 3 | 0.001 | 0.5084060496754117 | 0.3563646740383572 | 0.09457878602875604 | 0.32678870028919643 | 0.4492427276240455 | 0.19155073165893555 | 0.3563646740383572 | 0.30823153257369995 | 0.2178763449192047 | 0.03594436123967171 | 0.20229224860668182 | 0.4395522177219391 | 0.1724669635295868 | 0.2178763449192047 |
| 5 | 4 | 0.001 | 0.46196970012452865 | 0.2874237828784519 | 0.10073509646786584 | 0.3864523735311296 | 0.4910557005140517 | 0.2566720247268677 | 0.2874237828784519 | 0.2588622272014618 | 0.1819354146718979 | 0.02867879904806614 | 0.16302458941936493 | 0.44016343355178833 | 0.1725275069475174 | 0.1819354146718979 |
| 6 | 5 | 0.001 | 0.4498247702916463 | 0.2731182641453213 | 0.0863925533162223 | 0.3938671946525574 | 0.6069219443533156 | 0.33671536213821834 | 0.2731182641453213 | 0.3132787346839905 | 0.22292804718017578 | 0.02889658883213997 | 0.2158222645521164 | 0.44235706329345703 | 0.17352235317230225 | 0.22292804718017578 |
| 7 | 6 | 0.001 | 0.42692894405788845 | 0.28966741760571796 | 0.05804820607105891 | 0.3025594221221076 | 0.5698766128884422 | 0.3100252277735207 | 0.28966741760571796 | 0.4311088025569916 | 0.3014686703681946 | 0.060949672013521194 | 0.30897605419158936 | 0.4416200518608093 | 0.17345383763313293 | 0.3014686703681946 |
| 8 | 7 | 0.001 | 0.36648913555675083 | 0.2673240436447991 | 0.04492858507566982 | 0.2210414773888058 | 0.40499014324612087 | 0.1858146521780226 | 0.2673240436447991 | 0.3253621459007263 | 0.2316536009311676 | 0.031499434262514114 | 0.22455067932605743 | 0.43930211663246155 | 0.17284171283245087 | 0.2316536009311676 |
| 9 | 8 | 0.001 | 0.3705151147312588 | 0.2639704677793715 | 0.056356401907073125 | 0.23181239929464129 | 0.40243885583347744 | 0.1668036257227262 | 0.2639704677793715 | 0.2693358361721039 | 0.19200395047664642 | 0.023184901103377342 | 0.17564474046230316 | 0.43946224451065063 | 0.17288772761821747 | 0.19200395047664642 |
| 10 | 9 | 0.001 | 0.3809369206428528 | 0.238957146803538 | 0.05438671095503701 | 0.32686107357343036 | 0.5658377408981323 | 0.32192010349697536 | 0.238957146803538 | 0.22007109224796295 | 0.15654480457305908 | 0.01698182336986065 | 0.1330064833164215 | 0.43889322876930237 | 0.17233915627002716 | 0.15654480457305908 |
| 11 | 10 | 0.001 | 0.3033063875304328 | 0.20820601118935478 | 0.03028729951216115 | 0.2125823481215371 | 0.4863186809751723 | 0.244431518846088 | 0.20820601118935478 | 0.2353043407201767 | 0.16689710319042206 | 0.020381871610879898 | 0.14587727189064026 | 0.43858274817466736 | 0.17178986966609955 | 0.16689710319042206 |
| 12 | 11 | 0.001 | 0.3418840931521522 | 0.21859833929273817 | 0.041131472835938133 | 0.3211692141162025 | 0.4186662236849467 | 0.1973548432191213 | 0.21859833929273817 | 0.2819516360759735 | 0.19871805608272552 | 0.029886296018958092 | 0.1862599402666092 | 0.43857088685035706 | 0.17136642336845398 | 0.19871805608272552 |
| 13 | 12 | 0.001 | 0.327799528837204 | 0.2257505324151781 | 0.038094287945164576 | 0.24139907293849522 | 0.4269571900367737 | 0.19686741133530936 | 0.2257505324151781 | 0.2265533208847046 | 0.15818463265895844 | 0.02364269271492958 | 0.13944414258003235 | 0.43839970231056213 | 0.17068645358085632 | 0.15818463265895844 |
| 14 | 13 | 0.001 | 0.5482124156422086 | 0.2889212518930435 | 0.05876548960804939 | 0.7335240874025557 | 0.7617671754625108 | 0.4734871983528137 | 0.2889212518930435 | 0.13387161493301392 | 0.09191606938838959 | 0.01390319224447012 | 0.05341717228293419 | 0.4380643963813782 | 0.1701544225215912 | 0.09191606938838959 |
| 15 | 14 | 0.001 | 0.2683628300825755 | 0.1934465699725681 | 0.03215170403321584 | 0.1588393354581462 | 0.3651085015800264 | 0.16354632439712682 | 0.1934465699725681 | 0.11752602458000183 | 0.08033295720815659 | 0.0075136758387088776 | 0.0473531074821949 | 0.43740665912628174 | 0.16951507329940796 | 0.08033295720815659 |
| 16 | 15 | 0.001 | 0.23245521552032894 | 0.15845761530929142 | 0.021862993792941172 | 0.1661414752403895 | 0.4185062315728929 | 0.17951083762778175 | 0.15845761530929142 | 0.13540877401828766 | 0.09327995032072067 | 0.009294009767472744 | 0.06379763782024384 | 0.4362388551235199 | 0.16890482604503632 | 0.09327995032072067 |
| 17 | 16 | 0.001 | 0.2540251049730513 | 0.16881393061743843 | 0.02455571148958471 | 0.1829415543211831 | 0.4814679291513231 | 0.2550777710146374 | 0.16881393061743843 | 0.24452388286590576 | 0.17012111842632294 | 0.027872953563928604 | 0.15593497455120087 | 0.43555423617362976 | 0.16831809282302856 | 0.17012111842632294 |
| 18 | 17 | 0.001 | 0.27790621254179215 | 0.16291005578305987 | 0.02768930456497603 | 0.27147142092386883 | 0.5733835763401456 | 0.3216428938839171 | 0.16291005578305987 | 0.23126113414764404 | 0.16358613967895508 | 0.019583800807595253 | 0.14588342607021332 | 0.4346035122871399 | 0.16748279333114624 | 0.16358613967895508 |
| 19 | 18 | 0.001 | 0.21714572608470917 | 0.16336159076955584 | 0.02207756083872583 | 0.11655174030197991 | 0.29744883709483677 | 0.09367910772562027 | 0.16336159076955584 | 0.21335509419441223 | 0.15434198081493378 | 0.013381856493651867 | 0.12396419048309326 | 0.43387162685394287 | 0.1662997454404831 | 0.15434198081493378 |
| 20 | 19 | 0.001 | 0.39585938718583846 | 0.23502571880817413 | 0.03807514740361108 | 0.43226980169614154 | 0.5700339277585348 | 0.33255258699258167 | 0.23502571880817413 | 0.29088473320007324 | 0.1979036182165146 | 0.050344835966825485 | 0.18606995046138763 | 0.4335789680480957 | 0.16567720472812653 | 0.1979036182165146 |
| 21 | 20 | 0.001 | 0.26503710283173454 | 0.18777255879508126 | 0.03789552880658044 | 0.15700330336888632 | 0.36711519294314915 | 0.16105001833703783 | 0.18777255879508126 | 0.3211463689804077 | 0.22426843643188477 | 0.04961967095732689 | 0.20329146087169647 | 0.4336654543876648 | 0.16470572352409363 | 0.22426843643188477 |
| 22 | 21 | 0.001 | 0.28275859852631885 | 0.18599259356657663 | 0.043205282702628106 | 0.20048580318689346 | 0.4617711802323659 | 0.22377558714813656 | 0.18599259356657663 | 0.26557254791259766 | 0.19436083734035492 | 0.022131552919745445 | 0.15585075318813324 | 0.43292519450187683 | 0.1639096587896347 | 0.19436083734035492 |
| 23 | 22 | 0.001 | 0.21910542911953396 | 0.14179434875647226 | 0.016369602125551965 | 0.16523817347155678 | 0.5044849514961243 | 0.2536436948511336 | 0.14179434875647226 | 0.2662939727306366 | 0.19275937974452972 | 0.025155413895845413 | 0.15944524109363556 | 0.4315638244152069 | 0.16297321021556854 | 0.19275937974452972 |
| 24 | 23 | 0.0005 | 0.3775199121899075 | 0.2587335771984524 | 0.04730055698504051 | 0.2791730182038413 | 0.45998597972922856 | 0.23086444040139517 | 0.2587335771984524 | 0.24067305028438568 | 0.1702818125486374 | 0.027465825900435448 | 0.1423315852880478 | 0.43073099851608276 | 0.16307000815868378 | 0.1702818125486374 |
| 25 | 24 | 0.0005 | 0.12755481650431952 | 0.09668536235888799 | 0.006956045328277267 | 0.05454847796095742 | 0.30247317420111763 | 0.09360233859883414 | 0.09668536235888799 | 0.21739451587200165 | 0.15576092898845673 | 0.02215108834207058 | 0.1178358793258667 | 0.4309665262699127 | 0.16340160369873047 | 0.15576092898845673 |
| 26 | 25 | 0.0005 | 0.2033023022943073 | 0.13513341546058655 | 0.014085318272312483 | 0.12979511668284735 | 0.5040803915924497 | 0.2711007396380107 | 0.13513341546058655 | 0.22780413925647736 | 0.16796176135540009 | 0.01980750262737274 | 0.11519914120435715 | 0.43173280358314514 | 0.1637372523546219 | 0.16796176135540009 |
| 27 | 26 | 0.0005 | 0.26767876081996494 | 0.19527330001195273 | 0.029956953910489876 | 0.14093264937400818 | 0.4134024911456638 | 0.19583487345112693 | 0.19527330001195273 | 0.22256356477737427 | 0.16064496338367462 | 0.0197360347956419 | 0.12349464744329453 | 0.43188977241516113 | 0.16440483927726746 | 0.16064496338367462 |
| 28 | 27 | 0.0005 | 0.29266027278370327 | 0.21245999303128985 | 0.037366345110866755 | 0.15680206815401712 | 0.4156636761294471 | 0.19693358656432894 | 0.21245999303128985 | 0.23951780796051025 | 0.1713121384382248 | 0.026933521032333374 | 0.13419118523597717 | 0.4317554533481598 | 0.16476942598819733 | 0.1713121384382248 |
| 29 | 28 | 0.0005 | 0.2840205712450875 | 0.1834611776802275 | 0.03268412335051431 | 0.22491087267796198 | 0.502896871831682 | 0.25805431852738064 | 0.1834611776802275 | 0.20573638379573822 | 0.1483912318944931 | 0.018318505957722664 | 0.10800518840551376 | 0.43159055709838867 | 0.16473759710788727 | 0.1483912318944931 |
| 30 | 29 | 0.0005 | 0.18021956914001042 | 0.1047939153181182 | 0.012183074632452594 | 0.14656837284564972 | 0.5704893204900954 | 0.31154681907759774 | 0.1047939153181182 | 0.17089061439037323 | 0.12431001663208008 | 0.01182954479008913 | 0.07795087993144989 | 0.43144071102142334 | 0.16469746828079224 | 0.12431001663208008 |