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etth2_m.log
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etth2_m.log
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Dataset: ETTh2
Arguments: Namespace(alpha=0.0005, archive='forecast_csv', batch_size=128, dataset='ETTh2', epochs=None, eval=True, gpu=1, iters=None, kernels=[1, 2, 4, 8, 16, 32, 64, 128], lr=0.001, max_threads=8, max_train_length=201, repr_dims=320, run_name='forecast_multivar', save_every=None, seed=0)
input_fc.weight [14, 64] Place(gpu:1)
input_fc.bias [64] Place(gpu:1)
feature_extractor.net.0.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.0.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.0.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.0.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.1.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.1.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.1.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.1.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.2.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.2.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.2.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.2.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.3.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.3.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.3.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.3.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.4.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.4.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.4.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.4.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.5.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.5.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.5.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.5.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.6.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.6.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.6.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.6.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.7.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.7.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.7.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.7.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.8.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.8.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.8.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.8.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.9.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.9.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.9.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.9.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.10.conv1.conv.weight [320, 64, 3] Place(gpu:1)
feature_extractor.net.10.conv1.conv.bias [320] Place(gpu:1)
feature_extractor.net.10.conv2.conv.weight [320, 320, 3] Place(gpu:1)
feature_extractor.net.10.conv2.conv.bias [320] Place(gpu:1)
feature_extractor.net.10.projector.weight [320, 64, 1] Place(gpu:1)
feature_extractor.net.10.projector.bias [320] Place(gpu:1)
tfd.0.weight [160, 320, 1] Place(gpu:1)
tfd.0.bias [160] Place(gpu:1)
tfd.1.weight [160, 320, 2] Place(gpu:1)
tfd.1.bias [160] Place(gpu:1)
tfd.2.weight [160, 320, 4] Place(gpu:1)
tfd.2.bias [160] Place(gpu:1)
tfd.3.weight [160, 320, 8] Place(gpu:1)
tfd.3.bias [160] Place(gpu:1)
tfd.4.weight [160, 320, 16] Place(gpu:1)
tfd.4.bias [160] Place(gpu:1)
tfd.5.weight [160, 320, 32] Place(gpu:1)
tfd.5.bias [160] Place(gpu:1)
tfd.6.weight [160, 320, 64] Place(gpu:1)
tfd.6.bias [160] Place(gpu:1)
tfd.7.weight [160, 320, 128] Place(gpu:1)
tfd.7.bias [160] Place(gpu:1)
sfd.0.weight [101, 320, 160] Place(gpu:1)
sfd.0.bias [101, 160] Place(gpu:1)
---------------------------------------------------------------
input_fc.weight [14, 64] Place(gpu:1)
input_fc.bias [64] Place(gpu:1)
feature_extractor.net.0.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.0.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.0.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.0.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.1.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.1.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.1.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.1.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.2.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.2.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.2.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.2.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.3.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.3.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.3.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.3.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.4.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.4.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.4.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.4.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.5.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.5.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.5.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.5.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.6.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.6.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.6.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.6.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.7.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.7.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.7.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.7.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.8.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.8.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.8.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.8.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.9.conv1.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.9.conv1.conv.bias [64] Place(gpu:1)
feature_extractor.net.9.conv2.conv.weight [64, 64, 3] Place(gpu:1)
feature_extractor.net.9.conv2.conv.bias [64] Place(gpu:1)
feature_extractor.net.10.conv1.conv.weight [320, 64, 3] Place(gpu:1)
feature_extractor.net.10.conv1.conv.bias [320] Place(gpu:1)
feature_extractor.net.10.conv2.conv.weight [320, 320, 3] Place(gpu:1)
feature_extractor.net.10.conv2.conv.bias [320] Place(gpu:1)
feature_extractor.net.10.projector.weight [320, 64, 1] Place(gpu:1)
feature_extractor.net.10.projector.bias [320] Place(gpu:1)
tfd.0.weight [160, 320, 1] Place(gpu:1)
tfd.0.bias [160] Place(gpu:1)
tfd.1.weight [160, 320, 2] Place(gpu:1)
tfd.1.bias [160] Place(gpu:1)
tfd.2.weight [160, 320, 4] Place(gpu:1)
tfd.2.bias [160] Place(gpu:1)
tfd.3.weight [160, 320, 8] Place(gpu:1)
tfd.3.bias [160] Place(gpu:1)
tfd.4.weight [160, 320, 16] Place(gpu:1)
tfd.4.bias [160] Place(gpu:1)
tfd.5.weight [160, 320, 32] Place(gpu:1)
tfd.5.bias [160] Place(gpu:1)
tfd.6.weight [160, 320, 64] Place(gpu:1)
tfd.6.bias [160] Place(gpu:1)
tfd.7.weight [160, 320, 128] Place(gpu:1)
tfd.7.bias [160] Place(gpu:1)
sfd.0.weight [101, 320, 160] Place(gpu:1)
sfd.0.bias [101, 160] Place(gpu:1)
Epoch #0: loss=0.1686689853668213
Epoch #1: loss=3.00134015083313
Epoch #2: loss=2.4072265625
Epoch #3: loss=3.7684526443481445
Epoch #4: loss=3.410202980041504
Epoch #5: loss=2.3453750610351562
Epoch #6: loss=0.9011410474777222
Epoch #7: loss=3.4433748722076416
Epoch #8: loss=3.7243385314941406
Epoch #9: loss=3.8021280765533447
Epoch #10: loss=3.5284600257873535
Epoch #11: loss=3.595435857772827
Epoch #12: loss=3.2183330059051514
Epoch #13: loss=3.6629891395568848
Epoch #14: loss=2.6892635822296143
Epoch #15: loss=3.498767614364624
Epoch #16: loss=3.0678608417510986
Epoch #17: loss=3.358125925064087
Epoch #18: loss=1.7121905088424683
Epoch #19: loss=1.8566197156906128
Epoch #20: loss=3.6955018043518066
Epoch #21: loss=4.184655666351318
Epoch #22: loss=1.9845281839370728
Epoch #23: loss=3.3782777786254883
Epoch #24: loss=2.2936110496520996
Epoch #25: loss=3.311971664428711
Epoch #26: loss=0.41259798407554626
Epoch #27: loss=4.086854457855225
Epoch #28: loss=2.695603132247925
Epoch #29: loss=4.9434380531311035
Epoch #30: loss=4.167758464813232
Epoch #31: loss=2.1912684440612793
Epoch #32: loss=3.859361410140991
Epoch #33: loss=1.8642219305038452
Epoch #34: loss=2.9921631813049316
Epoch #35: loss=3.340630054473877
Epoch #36: loss=3.906376838684082
Epoch #37: loss=2.195579767227173
Epoch #38: loss=3.3464958667755127
Epoch #39: loss=3.3209052085876465
Epoch #40: loss=3.3660905361175537
Epoch #41: loss=3.509338617324829
Epoch #42: loss=3.5456066131591797
Epoch #43: loss=3.321237325668335
Epoch #44: loss=1.4223899841308594
Epoch #45: loss=3.6112489700317383
Epoch #46: loss=3.3930718898773193
Epoch #47: loss=2.6219735145568848
Epoch #48: loss=0.6850923299789429
Epoch #49: loss=2.428190231323242
Epoch #50: loss=3.1926395893096924
Epoch #51: loss=3.1249780654907227
Epoch #52: loss=1.8190746307373047
Epoch #53: loss=2.256181478500366
Epoch #54: loss=2.9868075847625732
Epoch #55: loss=3.6750221252441406
Epoch #56: loss=1.501184344291687
Epoch #57: loss=2.437471389770508
Epoch #58: loss=3.0435521602630615
Epoch #59: loss=3.1758008003234863
Epoch #60: loss=1.4993317127227783
Epoch #61: loss=2.8578414916992188
Epoch #62: loss=0.7999175190925598
Epoch #63: loss=2.9817543029785156
Epoch #64: loss=1.1907676458358765
Epoch #65: loss=2.54372239112854
Epoch #66: loss=3.0747947692871094
Epoch #67: loss=2.9823784828186035
Epoch #68: loss=0.792251706123352
Epoch #69: loss=2.6251237392425537
Epoch #70: loss=2.0194056034088135
Epoch #71: loss=1.1332793235778809
Epoch #72: loss=2.886744499206543
Epoch #73: loss=3.385383129119873
Epoch #74: loss=3.3740086555480957
Epoch #75: loss=2.9368598461151123
Epoch #76: loss=3.3155171871185303
Epoch #77: loss=2.6257712841033936
Epoch #78: loss=1.97184419631958
Epoch #79: loss=2.843252658843994
Epoch #80: loss=2.663405179977417
Epoch #81: loss=0.6034182906150818
Epoch #82: loss=2.819183588027954
Epoch #83: loss=2.1373519897460938
Epoch #84: loss=3.0106394290924072
Epoch #85: loss=3.313993453979492
Epoch #86: loss=1.8324631452560425
Epoch #87: loss=2.7875430583953857
Epoch #88: loss=0.7170885801315308
Epoch #89: loss=3.032667875289917
Epoch #90: loss=2.6582822799682617
Epoch #91: loss=0.46522843837738037
Epoch #92: loss=1.9806610345840454
Epoch #93: loss=1.8775962591171265
Epoch #94: loss=3.584620952606201
Epoch #95: loss=3.1239521503448486
Epoch #96: loss=1.8943419456481934
Epoch #97: loss=2.868809938430786
Epoch #98: loss=2.277486801147461
Epoch #99: loss=2.061681032180786
Epoch #100: loss=2.04632830619812
Epoch #101: loss=1.1941276788711548
Epoch #102: loss=2.707876205444336
Epoch #103: loss=1.9622840881347656
Epoch #104: loss=2.136780023574829
Epoch #105: loss=0.5737931132316589
Epoch #106: loss=3.384049654006958
Epoch #107: loss=2.382591962814331
Epoch #108: loss=0.5669494867324829
Epoch #109: loss=2.9600789546966553
Epoch #110: loss=2.42598819732666
Epoch #111: loss=1.256191372871399
Epoch #112: loss=1.32308030128479
Epoch #113: loss=1.7981699705123901
Epoch #114: loss=1.7971526384353638
Epoch #115: loss=2.5496113300323486
Epoch #116: loss=2.3771393299102783
Epoch #117: loss=0.9436567425727844
Epoch #118: loss=3.063953399658203
Epoch #119: loss=2.1216320991516113
Epoch #120: loss=1.4731980562210083
Epoch #121: loss=3.0670735836029053
Epoch #122: loss=2.576749563217163
Epoch #123: loss=1.7317465543746948
Epoch #124: loss=0.5570505261421204
Epoch #125: loss=1.4953051805496216
Epoch #126: loss=2.4114606380462646
Epoch #127: loss=3.0462405681610107
Epoch #128: loss=2.0998988151550293
Epoch #129: loss=3.1435394287109375
Epoch #130: loss=2.2577569484710693
Epoch #131: loss=1.314579963684082
Epoch #132: loss=2.4348418712615967
Epoch #133: loss=2.495985507965088
Epoch #134: loss=2.123516798019409
Epoch #135: loss=1.0765551328659058
Epoch #136: loss=2.5685372352600098
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Epoch #592: loss=0.3275487422943115
Epoch #593: loss=0.232466459274292
Epoch #594: loss=1.0000075101852417
Epoch #595: loss=0.08685633540153503
Epoch #596: loss=2.314032793045044
Epoch #597: loss=2.267106771469116
Epoch #598: loss=0.05862567201256752
Epoch #599: loss=0.49797868728637695
Training time: 0:04:25.098797
Evaluation result: {'ours': {24: {'norm': {'MSE': 2.4528299923522163, 'MAE': 1.219710765526419}, 'raw': {'MSE': 107.14113923364704, 'MAE': 8.379669259576762}}, 48: {'norm': {'MSE': 2.977137724189014, 'MAE': 1.3659685733906453}, 'raw': {'MSE': 120.88592385813011, 'MAE': 9.045432158844639}}, 168: {'norm': {'MSE': 4.39788078905017, 'MAE': 1.719169576732627}, 'raw': {'MSE': 184.24947286413905, 'MAE': 11.329444767686049}}, 336: {'norm': {'MSE': 3.7540480264602443, 'MAE': 1.5875437313562881}, 'raw': {'MSE': 200.0848977071667, 'MAE': 11.174915538536135}}, 720: {'norm': {'MSE': 3.206752187086787, 'MAE': 1.453380457969707}, 'raw': {'MSE': 173.68748086734024, 'MAE': 10.275458799368074}}}, 'encoder_infer_time': 4.378287315368652, 'lr_train_time': {24: 1.804105520248413, 48: 2.4277961254119873, 168: 4.152881860733032, 336: 5.334191083908081, 720: 8.715614080429077}, 'lr_infer_time': {24: 0.009588956832885742, 48: 0.008199453353881836, 168: 0.02251267433166504, 336: 0.030108213424682617, 720: 0.10231804847717285}}
Finished.