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etth1_m.log
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Dataset: ETTh1
Arguments: Namespace(alpha=0.0005, archive='forecast_csv', batch_size=128, dataset='ETTh1', epochs=None, eval=True, gpu=0, 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:0)
input_fc.bias [64] Place(gpu:0)
feature_extractor.net.0.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.0.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.0.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.0.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.1.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.1.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.1.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.1.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.2.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.2.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.2.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.2.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.3.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.3.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.3.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.3.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.4.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.4.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.4.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.4.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.5.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.5.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.5.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.5.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.6.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.6.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.6.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.6.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.7.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.7.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.7.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.7.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.8.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.8.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.8.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.8.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.9.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.9.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.9.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.9.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.10.conv1.conv.weight [320, 64, 3] Place(gpu:0)
feature_extractor.net.10.conv1.conv.bias [320] Place(gpu:0)
feature_extractor.net.10.conv2.conv.weight [320, 320, 3] Place(gpu:0)
feature_extractor.net.10.conv2.conv.bias [320] Place(gpu:0)
feature_extractor.net.10.projector.weight [320, 64, 1] Place(gpu:0)
feature_extractor.net.10.projector.bias [320] Place(gpu:0)
tfd.0.weight [160, 320, 1] Place(gpu:0)
tfd.0.bias [160] Place(gpu:0)
tfd.1.weight [160, 320, 2] Place(gpu:0)
tfd.1.bias [160] Place(gpu:0)
tfd.2.weight [160, 320, 4] Place(gpu:0)
tfd.2.bias [160] Place(gpu:0)
tfd.3.weight [160, 320, 8] Place(gpu:0)
tfd.3.bias [160] Place(gpu:0)
tfd.4.weight [160, 320, 16] Place(gpu:0)
tfd.4.bias [160] Place(gpu:0)
tfd.5.weight [160, 320, 32] Place(gpu:0)
tfd.5.bias [160] Place(gpu:0)
tfd.6.weight [160, 320, 64] Place(gpu:0)
tfd.6.bias [160] Place(gpu:0)
tfd.7.weight [160, 320, 128] Place(gpu:0)
tfd.7.bias [160] Place(gpu:0)
sfd.0.weight [101, 320, 160] Place(gpu:0)
sfd.0.bias [101, 160] Place(gpu:0)
---------------------------------------------------------------
input_fc.weight [14, 64] Place(gpu:0)
input_fc.bias [64] Place(gpu:0)
feature_extractor.net.0.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.0.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.0.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.0.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.1.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.1.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.1.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.1.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.2.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.2.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.2.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.2.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.3.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.3.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.3.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.3.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.4.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.4.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.4.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.4.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.5.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.5.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.5.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.5.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.6.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.6.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.6.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.6.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.7.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.7.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.7.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.7.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.8.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.8.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.8.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.8.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.9.conv1.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.9.conv1.conv.bias [64] Place(gpu:0)
feature_extractor.net.9.conv2.conv.weight [64, 64, 3] Place(gpu:0)
feature_extractor.net.9.conv2.conv.bias [64] Place(gpu:0)
feature_extractor.net.10.conv1.conv.weight [320, 64, 3] Place(gpu:0)
feature_extractor.net.10.conv1.conv.bias [320] Place(gpu:0)
feature_extractor.net.10.conv2.conv.weight [320, 320, 3] Place(gpu:0)
feature_extractor.net.10.conv2.conv.bias [320] Place(gpu:0)
feature_extractor.net.10.projector.weight [320, 64, 1] Place(gpu:0)
feature_extractor.net.10.projector.bias [320] Place(gpu:0)
tfd.0.weight [160, 320, 1] Place(gpu:0)
tfd.0.bias [160] Place(gpu:0)
tfd.1.weight [160, 320, 2] Place(gpu:0)
tfd.1.bias [160] Place(gpu:0)
tfd.2.weight [160, 320, 4] Place(gpu:0)
tfd.2.bias [160] Place(gpu:0)
tfd.3.weight [160, 320, 8] Place(gpu:0)
tfd.3.bias [160] Place(gpu:0)
tfd.4.weight [160, 320, 16] Place(gpu:0)
tfd.4.bias [160] Place(gpu:0)
tfd.5.weight [160, 320, 32] Place(gpu:0)
tfd.5.bias [160] Place(gpu:0)
tfd.6.weight [160, 320, 64] Place(gpu:0)
tfd.6.bias [160] Place(gpu:0)
tfd.7.weight [160, 320, 128] Place(gpu:0)
tfd.7.bias [160] Place(gpu:0)
sfd.0.weight [101, 320, 160] Place(gpu:0)
sfd.0.bias [101, 160] Place(gpu:0)
Epoch #0: loss=0.09111425280570984
Epoch #1: loss=2.8691141605377197
Epoch #2: loss=2.083372116088867
Epoch #3: loss=3.277621030807495
Epoch #4: loss=3.0168843269348145
Epoch #5: loss=2.0406439304351807
Epoch #6: loss=0.49195176362991333
Epoch #7: loss=3.0788066387176514
Epoch #8: loss=3.4395735263824463
Epoch #9: loss=3.126086473464966
Epoch #10: loss=3.3078372478485107
Epoch #11: loss=3.280325412750244
Epoch #12: loss=3.0605826377868652
Epoch #13: loss=4.142418384552002
Epoch #14: loss=2.742614984512329
Epoch #15: loss=3.512704610824585
Epoch #16: loss=2.7894647121429443
Epoch #17: loss=3.245826244354248
Epoch #18: loss=1.2596560716629028
Epoch #19: loss=1.5481983423233032
Epoch #20: loss=3.6840829849243164
Epoch #21: loss=3.7506349086761475
Epoch #22: loss=1.5797889232635498
Epoch #23: loss=3.3448026180267334
Epoch #24: loss=2.109933853149414
Epoch #25: loss=2.7903077602386475
Epoch #26: loss=0.39068901538848877
Epoch #27: loss=4.168085098266602
Epoch #28: loss=2.4162232875823975
Epoch #29: loss=4.961215019226074
Epoch #30: loss=4.404392719268799
Epoch #31: loss=1.599875807762146
Epoch #32: loss=3.855240821838379
Epoch #33: loss=1.4725024700164795
Epoch #34: loss=2.9297449588775635
Epoch #35: loss=2.8601760864257812
Epoch #36: loss=3.111814498901367
Epoch #37: loss=1.6526697874069214
Epoch #38: loss=2.9545788764953613
Epoch #39: loss=3.246351480484009
Epoch #40: loss=2.7546966075897217
Epoch #41: loss=3.290530204772949
Epoch #42: loss=3.283722162246704
Epoch #43: loss=2.8420872688293457
Epoch #44: loss=1.366636037826538
Epoch #45: loss=3.623574733734131
Epoch #46: loss=2.8842620849609375
Epoch #47: loss=2.6538796424865723
Epoch #48: loss=0.5925517678260803
Epoch #49: loss=2.43076229095459
Epoch #50: loss=3.1624419689178467
Epoch #51: loss=2.6967852115631104
Epoch #52: loss=1.6282713413238525
Epoch #53: loss=1.9500163793563843
Epoch #54: loss=2.8654000759124756
Epoch #55: loss=3.3310611248016357
Epoch #56: loss=1.334876298904419
Epoch #57: loss=2.3147904872894287
Epoch #58: loss=2.6439006328582764
Epoch #59: loss=2.764448404312134
Epoch #60: loss=1.2764679193496704
Epoch #61: loss=2.607144355773926
Epoch #62: loss=0.9391728043556213
Epoch #63: loss=2.9310693740844727
Epoch #64: loss=0.9385104179382324
Epoch #65: loss=2.2236328125
Epoch #66: loss=2.820805311203003
Epoch #67: loss=2.638841152191162
Epoch #68: loss=0.5777603983879089
Epoch #69: loss=2.3902623653411865
Epoch #70: loss=1.929857850074768
Epoch #71: loss=0.947801947593689
Epoch #72: loss=2.555757761001587
Epoch #73: loss=3.0610814094543457
Epoch #74: loss=2.8999714851379395
Epoch #75: loss=2.406397819519043
Epoch #76: loss=2.9204978942871094
Epoch #77: loss=2.3311429023742676
Epoch #78: loss=1.6879485845565796
Epoch #79: loss=2.7238073348999023
Epoch #80: loss=2.8018600940704346
Epoch #81: loss=0.35898175835609436
Epoch #82: loss=2.6909339427948
Epoch #83: loss=1.9859949350357056
Epoch #84: loss=2.512671709060669
Epoch #85: loss=2.9951772689819336
Epoch #86: loss=1.6365909576416016
Epoch #87: loss=2.3709938526153564
Epoch #88: loss=0.37241148948669434
Epoch #89: loss=2.918191909790039
Epoch #90: loss=2.292848825454712
Epoch #91: loss=0.30936112999916077
Epoch #92: loss=1.8904955387115479
Epoch #93: loss=1.8019124269485474
Epoch #94: loss=3.1220943927764893
Epoch #95: loss=2.747682809829712
Epoch #96: loss=1.5725566148757935
Epoch #97: loss=2.561310052871704
Epoch #98: loss=1.9251378774642944
Epoch #99: loss=1.3752940893173218
Epoch #100: loss=1.850577712059021
Epoch #101: loss=0.9701893329620361
Epoch #102: loss=2.4131996631622314
Epoch #103: loss=1.7661441564559937
Epoch #104: loss=1.9325846433639526
Epoch #105: loss=0.5348603129386902
Epoch #106: loss=3.1510708332061768
Epoch #107: loss=2.0542612075805664
Epoch #108: loss=0.4018405079841614
Epoch #109: loss=2.8796849250793457
Epoch #110: loss=1.979877233505249
Epoch #111: loss=1.1571059226989746
Epoch #112: loss=1.3688604831695557
Epoch #113: loss=1.5067486763000488
Epoch #114: loss=1.5656039714813232
Epoch #115: loss=2.028669834136963
Epoch #116: loss=2.0880990028381348
Epoch #117: loss=0.8210785984992981
Epoch #118: loss=2.8849129676818848
Epoch #119: loss=1.7507858276367188
Epoch #120: loss=1.1782922744750977
Epoch #121: loss=2.6852521896362305
Epoch #122: loss=2.3905630111694336
Epoch #123: loss=1.4049745798110962
Epoch #124: loss=0.3098100423812866
Epoch #125: loss=0.967423677444458
Epoch #126: loss=1.9294534921646118
Epoch #127: loss=2.551966667175293
Epoch #128: loss=1.860701084136963
Epoch #129: loss=2.8927369117736816
Epoch #130: loss=1.6257636547088623
Epoch #131: loss=1.08293616771698
Epoch #132: loss=2.541367292404175
Epoch #133: loss=1.933260440826416
Epoch #134: loss=2.0057129859924316
Epoch #135: loss=0.9607201814651489
Epoch #136: loss=2.0358681678771973
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Epoch #590: loss=0.21996325254440308
Epoch #591: loss=1.0743025541305542
Epoch #592: loss=0.27322351932525635
Epoch #593: loss=0.1624365746974945
Epoch #594: loss=0.7956072688102722
Epoch #595: loss=0.07555535435676575
Epoch #596: loss=2.2748093605041504
Epoch #597: loss=2.032555103302002
Epoch #598: loss=0.05262380465865135
Epoch #599: loss=0.3489914834499359
Training time: 0:06:46.139698
Evaluation result: {'ours': {24: {'norm': {'MSE': 0.6712022097597823, 'MAE': 0.6298785035442218}, 'raw': {'MSE': 14.672419055347461, 'MAE': 2.411799145891094}}, 48: {'norm': {'MSE': 0.7119976694652735, 'MAE': 0.649215917576891}, 'raw': {'MSE': 15.703313804830877, 'MAE': 2.492265101674151}}, 168: {'norm': {'MSE': 0.8421198260449353, 'MAE': 0.7075989958089053}, 'raw': {'MSE': 17.201471636191393, 'MAE': 2.6339053946168525}}, 336: {'norm': {'MSE': 0.9783082661529101, 'MAE': 0.7703271602675434}, 'raw': {'MSE': 18.03709250905952, 'MAE': 2.7503146680246275}}, 720: {'norm': {'MSE': 1.1202768126045624, 'MAE': 0.8425831442248162}, 'raw': {'MSE': 18.93645978631521, 'MAE': 2.93128735386568}}}, 'encoder_infer_time': 6.251052379608154, 'lr_train_time': {24: 2.3134589195251465, 48: 2.5383174419403076, 168: 3.2391586303710938, 336: 5.604488372802734, 720: 8.689801454544067}, 'lr_infer_time': {24: 0.006848573684692383, 48: 0.009593009948730469, 168: 0.016357898712158203, 336: 0.042142391204833984, 720: 0.1010274887084961}}
Finished.