ettm1_s.log

Dataset: ETTm1
Arguments: Namespace(alpha=0.0005, archive='forecast_csv_univar', batch_size=128, dataset='ETTm1', epochs=None, eval=True, gpu=3, 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_univar', save_every=None, seed=0)
input_fc.weight	[8, 64]	Place(gpu:3)
input_fc.bias	[64]	Place(gpu:3)
feature_extractor.net.0.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.0.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.0.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.0.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.1.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.1.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.1.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.1.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.2.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.2.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.2.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.2.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.3.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.3.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.3.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.3.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.4.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.4.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.4.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.4.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.5.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.5.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.5.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.5.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.6.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.6.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.6.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.6.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.7.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.7.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.7.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.7.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.8.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.8.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.8.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.8.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.9.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.9.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.9.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.9.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.10.conv1.conv.weight	[320, 64, 3]	Place(gpu:3)
feature_extractor.net.10.conv1.conv.bias	[320]	Place(gpu:3)
feature_extractor.net.10.conv2.conv.weight	[320, 320, 3]	Place(gpu:3)
feature_extractor.net.10.conv2.conv.bias	[320]	Place(gpu:3)
feature_extractor.net.10.projector.weight	[320, 64, 1]	Place(gpu:3)
feature_extractor.net.10.projector.bias	[320]	Place(gpu:3)
tfd.0.weight	[160, 320, 1]	Place(gpu:3)
tfd.0.bias	[160]	Place(gpu:3)
tfd.1.weight	[160, 320, 2]	Place(gpu:3)
tfd.1.bias	[160]	Place(gpu:3)
tfd.2.weight	[160, 320, 4]	Place(gpu:3)
tfd.2.bias	[160]	Place(gpu:3)
tfd.3.weight	[160, 320, 8]	Place(gpu:3)
tfd.3.bias	[160]	Place(gpu:3)
tfd.4.weight	[160, 320, 16]	Place(gpu:3)
tfd.4.bias	[160]	Place(gpu:3)
tfd.5.weight	[160, 320, 32]	Place(gpu:3)
tfd.5.bias	[160]	Place(gpu:3)
tfd.6.weight	[160, 320, 64]	Place(gpu:3)
tfd.6.bias	[160]	Place(gpu:3)
tfd.7.weight	[160, 320, 128]	Place(gpu:3)
tfd.7.bias	[160]	Place(gpu:3)
sfd.0.weight	[101, 320, 160]	Place(gpu:3)
sfd.0.bias	[101, 160]	Place(gpu:3)
---------------------------------------------------------------
input_fc.weight	[8, 64]	Place(gpu:3)
input_fc.bias	[64]	Place(gpu:3)
feature_extractor.net.0.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.0.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.0.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.0.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.1.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.1.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.1.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.1.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.2.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.2.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.2.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.2.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.3.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.3.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.3.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.3.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.4.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.4.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.4.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.4.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.5.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.5.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.5.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.5.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.6.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.6.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.6.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.6.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.7.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.7.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.7.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.7.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.8.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.8.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.8.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.8.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.9.conv1.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.9.conv1.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.9.conv2.conv.weight	[64, 64, 3]	Place(gpu:3)
feature_extractor.net.9.conv2.conv.bias	[64]	Place(gpu:3)
feature_extractor.net.10.conv1.conv.weight	[320, 64, 3]	Place(gpu:3)
feature_extractor.net.10.conv1.conv.bias	[320]	Place(gpu:3)
feature_extractor.net.10.conv2.conv.weight	[320, 320, 3]	Place(gpu:3)
feature_extractor.net.10.conv2.conv.bias	[320]	Place(gpu:3)
feature_extractor.net.10.projector.weight	[320, 64, 1]	Place(gpu:3)
feature_extractor.net.10.projector.bias	[320]	Place(gpu:3)
tfd.0.weight	[160, 320, 1]	Place(gpu:3)
tfd.0.bias	[160]	Place(gpu:3)
tfd.1.weight	[160, 320, 2]	Place(gpu:3)
tfd.1.bias	[160]	Place(gpu:3)
tfd.2.weight	[160, 320, 4]	Place(gpu:3)
tfd.2.bias	[160]	Place(gpu:3)
tfd.3.weight	[160, 320, 8]	Place(gpu:3)
tfd.3.bias	[160]	Place(gpu:3)
tfd.4.weight	[160, 320, 16]	Place(gpu:3)
tfd.4.bias	[160]	Place(gpu:3)
tfd.5.weight	[160, 320, 32]	Place(gpu:3)
tfd.5.bias	[160]	Place(gpu:3)
tfd.6.weight	[160, 320, 64]	Place(gpu:3)
tfd.6.bias	[160]	Place(gpu:3)
tfd.7.weight	[160, 320, 128]	Place(gpu:3)
tfd.7.bias	[160]	Place(gpu:3)
sfd.0.weight	[101, 320, 160]	Place(gpu:3)
sfd.0.bias	[101, 160]	Place(gpu:3)
Epoch #0: loss=0.11843522638082504
Epoch #1: loss=1.8277668952941895
Epoch #2: loss=2.79183030128479
Epoch #3: loss=2.703508138656616
Epoch #4: loss=3.801107168197632
Epoch #5: loss=3.8332786560058594
Epoch #6: loss=1.7377299070358276
Epoch #7: loss=2.970073938369751
Epoch #8: loss=2.749394178390503
Epoch #9: loss=2.724374771118164
Epoch #10: loss=1.8475292921066284
Epoch #11: loss=2.2367184162139893
Epoch #12: loss=2.446979522705078
Epoch #13: loss=3.640838384628296
Epoch #14: loss=3.6744234561920166
Epoch #15: loss=3.283245801925659
Epoch #16: loss=2.683415651321411
Epoch #17: loss=2.3351871967315674
Epoch #18: loss=2.3934645652770996
Epoch #19: loss=1.1038947105407715
Epoch #20: loss=4.246838569641113
Epoch #21: loss=3.8223073482513428
Epoch #22: loss=0.8322081565856934
Epoch #23: loss=3.3173704147338867
Epoch #24: loss=3.762310028076172
Epoch #25: loss=3.7318766117095947
Epoch #26: loss=2.64896559715271
Epoch #27: loss=1.8413087129592896
Epoch #28: loss=1.9077503681182861
Epoch #29: loss=3.2373220920562744
Epoch #30: loss=3.314095973968506
Epoch #31: loss=3.4658377170562744
Epoch #32: loss=3.010157585144043
Epoch #33: loss=2.322514772415161
Epoch #34: loss=3.3709278106689453
Epoch #35: loss=2.720336437225342
Epoch #36: loss=2.6927907466888428
Epoch #37: loss=2.2407922744750977
Epoch #38: loss=0.9788379073143005
Epoch #39: loss=2.2023115158081055
Epoch #40: loss=1.4509133100509644
Epoch #41: loss=1.8751471042633057
Epoch #42: loss=2.18125581741333
Epoch #43: loss=2.8617868423461914
Epoch #44: loss=3.3886406421661377
Epoch #45: loss=2.328399896621704
Epoch #46: loss=2.505998373031616
Epoch #47: loss=2.5051321983337402
Epoch #48: loss=2.887152671813965
Epoch #49: loss=0.4074569046497345
Epoch #50: loss=3.4907937049865723
Epoch #51: loss=3.469280481338501
Epoch #52: loss=1.5518265962600708
Epoch #53: loss=1.9649385213851929
Epoch #54: loss=1.571131706237793
Epoch #55: loss=2.910233736038208
Epoch #56: loss=1.3152186870574951
Epoch #57: loss=0.7828530073165894
Epoch #58: loss=2.423433303833008
Epoch #59: loss=3.644284725189209
Epoch #60: loss=2.9834718704223633
Epoch #61: loss=2.320701837539673
Epoch #62: loss=2.5407373905181885
Epoch #63: loss=3.1801037788391113
Epoch #64: loss=1.6823266744613647
Epoch #65: loss=1.8642255067825317
Epoch #66: loss=1.3453682661056519
Epoch #67: loss=1.7351824045181274
Epoch #68: loss=2.4656569957733154
Epoch #69: loss=2.5529682636260986
Epoch #70: loss=1.894544243812561
Epoch #71: loss=2.4256818294525146
Epoch #72: loss=2.0470192432403564
Epoch #73: loss=2.8739969730377197
Epoch #74: loss=2.7863752841949463
Epoch #75: loss=3.1335015296936035
Epoch #76: loss=0.6245179176330566
Epoch #77: loss=2.8582985401153564
Epoch #78: loss=1.448458194732666
Epoch #79: loss=1.0646430253982544
Epoch #80: loss=0.5278778076171875
Epoch #81: loss=1.5681017637252808
Epoch #82: loss=2.96095609664917
Epoch #83: loss=0.6749690771102905
Epoch #84: loss=1.5615499019622803
Epoch #85: loss=2.7060346603393555
Epoch #86: loss=0.8981297612190247
Epoch #87: loss=1.004695177078247
Epoch #88: loss=2.8364875316619873
Epoch #89: loss=1.4120385646820068
Epoch #90: loss=0.8607433438301086
Epoch #91: loss=3.0315330028533936
Epoch #92: loss=0.5877979397773743
Epoch #93: loss=2.0449843406677246
Epoch #94: loss=3.1597275733947754
Epoch #95: loss=2.912795305252075
Epoch #96: loss=2.5201988220214844
Epoch #97: loss=1.0730087757110596
Epoch #98: loss=1.0764175653457642
Epoch #99: loss=0.5365325212478638
Epoch #100: loss=2.185209274291992
Epoch #101: loss=2.9488298892974854
Epoch #102: loss=2.5688321590423584
Epoch #103: loss=0.6106003522872925
Epoch #104: loss=2.0843136310577393
Epoch #105: loss=1.8345807790756226
Epoch #106: loss=2.186385154724121
Epoch #107: loss=1.122805118560791
Epoch #108: loss=0.7360092997550964
Epoch #109: loss=2.632427215576172
Epoch #110: loss=3.0409438610076904
Epoch #111: loss=1.780414342880249
Epoch #112: loss=2.700887441635132
Epoch #113: loss=0.7954797148704529
Epoch #114: loss=1.7156782150268555
Epoch #115: loss=0.5080925822257996
Epoch #116: loss=2.2168054580688477
Epoch #117: loss=1.5062389373779297
Epoch #118: loss=2.672144651412964
Epoch #119: loss=2.0266292095184326
Epoch #120: loss=1.5492393970489502
Epoch #121: loss=2.600205421447754
Epoch #122: loss=2.727911949157715
Epoch #123: loss=2.542983293533325
Epoch #124: loss=1.047398567199707
Epoch #125: loss=1.4774342775344849
Epoch #126: loss=2.1726412773132324
Epoch #127: loss=2.570969343185425
Epoch #128: loss=2.2245376110076904
Epoch #129: loss=0.8316956758499146
Epoch #130: loss=2.539680004119873
Epoch #131: loss=1.533679485321045
Epoch #132: loss=0.44481566548347473
Epoch #133: loss=1.176772952079773
Epoch #134: loss=2.3965530395507812
Epoch #135: loss=0.23889094591140747
Epoch #136: loss=1.7380203008651733
Epoch #137: loss=2.0905261039733887
Epoch #138: loss=1.310050368309021
Epoch #139: loss=2.1516175270080566
Epoch #140: loss=2.0520639419555664
Epoch #141: loss=2.36765456199646
Epoch #142: loss=2.3388452529907227
Epoch #143: loss=0.627987265586853
Epoch #144: loss=1.8431864976882935
Epoch #145: loss=2.0765998363494873
Epoch #146: loss=2.0465855598449707
Epoch #147: loss=1.8354823589324951
Epoch #148: loss=2.2087557315826416
Epoch #149: loss=2.8488805294036865
Epoch #150: loss=0.4364326298236847
Epoch #151: loss=0.9987381100654602
Epoch #152: loss=3.176581621170044
Epoch #153: loss=1.8448379039764404
Epoch #154: loss=2.135277271270752
Epoch #155: loss=3.0852222442626953
Epoch #156: loss=0.13450005650520325
Epoch #157: loss=1.3668183088302612
Epoch #158: loss=1.5310755968093872
Epoch #159: loss=1.9067327976226807
Epoch #160: loss=2.7904279232025146
Epoch #161: loss=0.4304373264312744
Epoch #162: loss=2.070711135864258
Epoch #163: loss=2.8412787914276123
Epoch #164: loss=0.6810687780380249
Epoch #165: loss=1.6165704727172852
Epoch #166: loss=1.3209171295166016
Epoch #167: loss=1.939107894897461
Epoch #168: loss=1.140006422996521
Epoch #169: loss=1.4561370611190796
Epoch #170: loss=2.4969043731689453
Epoch #171: loss=1.7403223514556885
Epoch #172: loss=0.40269628167152405
Epoch #173: loss=2.3579063415527344
Epoch #174: loss=1.5756760835647583
Epoch #175: loss=2.4492928981781006
Epoch #176: loss=0.7710087895393372
Epoch #177: loss=1.9526443481445312
Epoch #178: loss=0.14580722153186798
Epoch #179: loss=1.5469858646392822
Epoch #180: loss=2.0906152725219727
Epoch #181: loss=1.3158278465270996
Epoch #182: loss=1.0918744802474976
Epoch #183: loss=0.4380511939525604
Epoch #184: loss=0.7639846205711365
Epoch #185: loss=1.5941249132156372
Epoch #186: loss=2.5254383087158203
Epoch #187: loss=0.8623375296592712
Epoch #188: loss=1.191696047782898
Epoch #189: loss=2.1796088218688965
Epoch #190: loss=1.3146138191223145
Epoch #191: loss=1.9237631559371948
Epoch #192: loss=1.8286991119384766
Epoch #193: loss=2.4808263778686523
Epoch #194: loss=0.5642691254615784
Epoch #195: loss=1.4215202331542969
Epoch #196: loss=1.4273375272750854
Epoch #197: loss=0.9979309439659119
Epoch #198: loss=0.299687922000885
Epoch #199: loss=1.9904365539550781
Epoch #200: loss=1.4053666591644287
Epoch #201: loss=2.14337158203125
Epoch #202: loss=2.802582025527954
Epoch #203: loss=0.46608206629753113
Epoch #204: loss=2.4175186157226562
Epoch #205: loss=3.162022113800049
Epoch #206: loss=2.1086816787719727
Epoch #207: loss=1.1753896474838257
Epoch #208: loss=1.523287057876587
Epoch #209: loss=1.9624930620193481
Epoch #210: loss=1.022199034690857
Epoch #211: loss=2.3608558177948
Epoch #212: loss=2.102540969848633
Epoch #213: loss=0.8730144500732422
Epoch #214: loss=1.8086291551589966
Epoch #215: loss=1.6689891815185547
Epoch #216: loss=1.650709629058838
Epoch #217: loss=1.6967769861221313
Epoch #218: loss=1.3365306854248047
Epoch #219: loss=0.5985180735588074
Epoch #220: loss=2.07059383392334
Epoch #221: loss=1.9621860980987549
Epoch #222: loss=1.4655554294586182
Epoch #223: loss=1.7878878116607666
Epoch #224: loss=0.34171193838119507
Epoch #225: loss=0.847809910774231
Epoch #226: loss=0.6070874929428101
Epoch #227: loss=1.82553231716156
Epoch #228: loss=2.2682454586029053
Epoch #229: loss=0.6938998699188232
Epoch #230: loss=1.5990452766418457
Epoch #231: loss=0.8478929996490479
Epoch #232: loss=2.027925491333008
Epoch #233: loss=1.3467522859573364
Epoch #234: loss=2.2346420288085938
Epoch #235: loss=0.39189648628234863
Epoch #236: loss=1.255416989326477
Epoch #237: loss=1.2344352006912231
Epoch #238: loss=2.343782424926758
Epoch #239: loss=0.16867372393608093
Epoch #240: loss=2.216947078704834
Epoch #241: loss=2.0962696075439453
Epoch #242: loss=0.5953076481819153
Epoch #243: loss=1.0494322776794434
Epoch #244: loss=1.4874013662338257
Epoch #245: loss=0.865820586681366
Epoch #246: loss=1.12200927734375
Epoch #247: loss=0.9389716982841492
Epoch #248: loss=2.2618467807769775
Epoch #249: loss=1.1704736948013306
Epoch #250: loss=2.558295249938965
Epoch #251: loss=2.1612114906311035
Epoch #252: loss=2.524263858795166
Epoch #253: loss=0.08879154920578003
Epoch #254: loss=0.8826383948326111
Epoch #255: loss=0.6246809959411621
Epoch #256: loss=1.1254596710205078
Epoch #257: loss=2.1959547996520996
Epoch #258: loss=0.7267249822616577
Epoch #259: loss=0.7874476909637451
Epoch #260: loss=1.2081652879714966
Epoch #261: loss=2.5083279609680176
Epoch #262: loss=1.2916823625564575
Epoch #263: loss=1.8852620124816895
Epoch #264: loss=0.6370160579681396
Epoch #265: loss=1.4203839302062988
Epoch #266: loss=0.9227828979492188
Epoch #267: loss=0.15528368949890137
Epoch #268: loss=0.4356611371040344
Epoch #269: loss=0.8877428770065308
Epoch #270: loss=1.8709076642990112
Epoch #271: loss=2.7354977130889893
Epoch #272: loss=0.2806176245212555
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Epoch #299: loss=0.4229792654514313
Epoch #300: loss=1.259905457496643
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Epoch #307: loss=0.5239974856376648
Epoch #308: loss=0.32553333044052124
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Epoch #312: loss=0.6403199434280396
Epoch #313: loss=0.36726200580596924
Epoch #314: loss=2.481017827987671
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Epoch #328: loss=0.08512622117996216
Epoch #329: loss=0.4573615789413452
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Epoch #356: loss=0.631016194820404
Epoch #357: loss=0.40524864196777344
Epoch #358: loss=0.3420989513397217
Epoch #359: loss=0.44734519720077515
Epoch #360: loss=1.7932064533233643
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Epoch #367: loss=1.7927978038787842
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Epoch #370: loss=0.8036884069442749
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Epoch #386: loss=1.0435230731964111
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Epoch #388: loss=0.6536241769790649
Epoch #389: loss=1.4750674962997437
Epoch #390: loss=0.6581404209136963
Epoch #391: loss=0.3372896611690521
Epoch #392: loss=1.899290919303894
Epoch #393: loss=0.5243662595748901
Epoch #394: loss=1.9855352640151978
Epoch #395: loss=0.252471923828125
Epoch #396: loss=0.4740586578845978
Epoch #397: loss=0.12527647614479065
Epoch #398: loss=2.07645845413208
Epoch #399: loss=0.552707850933075
Epoch #400: loss=0.13505174219608307
Epoch #401: loss=1.2666444778442383
Epoch #402: loss=0.07663834095001221
Epoch #403: loss=0.4420102536678314
Epoch #404: loss=1.6694509983062744
Epoch #405: loss=2.4241483211517334
Epoch #406: loss=0.1364305168390274
Epoch #407: loss=1.2779884338378906
Epoch #408: loss=2.5688910484313965
Epoch #409: loss=0.6703439354896545
Epoch #410: loss=0.578875720500946
Epoch #411: loss=1.4538559913635254
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Epoch #413: loss=0.45895740389823914
Epoch #414: loss=1.4321025609970093
Epoch #415: loss=0.457249253988266
Epoch #416: loss=1.023820400238037
Epoch #417: loss=1.2430137395858765
Epoch #418: loss=1.2240701913833618
Epoch #419: loss=0.08881701529026031
Epoch #420: loss=0.8711148500442505
Epoch #421: loss=1.1966997385025024
Epoch #422: loss=0.49398675560951233
Epoch #423: loss=1.7761374711990356
Epoch #424: loss=0.22435474395751953
Epoch #425: loss=0.26050376892089844
Epoch #426: loss=0.5376260876655579
Epoch #427: loss=1.8172763586044312
Epoch #428: loss=0.5654950141906738
Epoch #429: loss=0.09855571389198303
Epoch #430: loss=1.6945897340774536
Epoch #431: loss=0.164350688457489
Epoch #432: loss=0.5612373948097229
Epoch #433: loss=0.14355039596557617
Epoch #434: loss=1.017423391342163
Epoch #435: loss=0.15559589862823486
Epoch #436: loss=0.4745950996875763
Epoch #437: loss=1.9617750644683838
Epoch #438: loss=1.4705896377563477
Epoch #439: loss=0.3658471405506134
Epoch #440: loss=0.12705688178539276
Epoch #441: loss=1.3016389608383179
Epoch #442: loss=2.2653911113739014
Epoch #443: loss=0.26656049489974976
Epoch #444: loss=0.6350735425949097
Epoch #445: loss=1.2058820724487305
Epoch #446: loss=0.950596272945404
Epoch #447: loss=0.5802498459815979
Epoch #448: loss=0.10341622680425644
Epoch #449: loss=0.22905299067497253
Epoch #450: loss=0.8215345144271851
Epoch #451: loss=1.1444591283798218
Epoch #452: loss=1.679955244064331
Epoch #453: loss=0.741042971611023
Epoch #454: loss=1.578665852546692
Epoch #455: loss=0.9486821293830872
Epoch #456: loss=0.18114179372787476
Epoch #457: loss=0.5567153096199036
Epoch #458: loss=0.624398946762085
Epoch #459: loss=1.8464607000350952
Epoch #460: loss=1.8883047103881836
Epoch #461: loss=0.5792050361633301
Epoch #462: loss=1.577878713607788
Epoch #463: loss=0.20002950727939606
Epoch #464: loss=1.1835561990737915
Epoch #465: loss=1.282407522201538
Epoch #466: loss=1.4321796894073486
Epoch #467: loss=1.46696937084198
Epoch #468: loss=2.0324931144714355
Epoch #469: loss=2.0107333660125732
Epoch #470: loss=1.267538070678711
Epoch #471: loss=0.15293578803539276
Epoch #472: loss=0.1966252326965332
Epoch #473: loss=0.9119217991828918
Epoch #474: loss=2.2322909832000732
Epoch #475: loss=0.3692364990711212
Epoch #476: loss=1.948358178138733
Epoch #477: loss=0.8744984269142151
Epoch #478: loss=1.9760141372680664
Epoch #479: loss=1.4724963903427124
Epoch #480: loss=0.39926955103874207
Epoch #481: loss=0.7129217386245728
Epoch #482: loss=0.08174116164445877
Epoch #483: loss=0.3032200336456299
Epoch #484: loss=1.1641861200332642
Epoch #485: loss=0.12908071279525757
Epoch #486: loss=0.15051011741161346
Epoch #487: loss=0.6401590704917908
Epoch #488: loss=0.22942590713500977
Epoch #489: loss=1.0134682655334473
Epoch #490: loss=1.6352280378341675
Epoch #491: loss=0.8040952682495117
Epoch #492: loss=1.127514123916626
Epoch #493: loss=0.10358463227748871
Epoch #494: loss=0.23166653513908386
Epoch #495: loss=0.8789159655570984
Epoch #496: loss=2.1173791885375977
Epoch #497: loss=0.18916508555412292
Epoch #498: loss=0.852955162525177
Epoch #499: loss=0.4668065309524536
Epoch #500: loss=0.2713424563407898
Epoch #501: loss=0.33426856994628906
Epoch #502: loss=0.6605483889579773
Epoch #503: loss=1.9571939706802368
Epoch #504: loss=1.3234220743179321
Epoch #505: loss=0.8575399518013
Epoch #506: loss=1.7117077112197876
Epoch #507: loss=0.08539796620607376
Epoch #508: loss=0.3002617359161377
Epoch #509: loss=1.17414128780365
Epoch #510: loss=1.7158211469650269
Epoch #511: loss=0.24160605669021606
Epoch #512: loss=0.5847464799880981
Epoch #513: loss=1.1194746494293213
Epoch #514: loss=1.0690641403198242
Epoch #515: loss=0.821635365486145
Epoch #516: loss=0.11807781457901001
Epoch #517: loss=2.2230398654937744
Epoch #518: loss=0.6995784044265747
Epoch #519: loss=1.115304946899414
Epoch #520: loss=2.019871473312378
Epoch #521: loss=0.26325929164886475
Epoch #522: loss=0.9442477226257324
Epoch #523: loss=0.17168143391609192
Epoch #524: loss=0.4351004660129547
Epoch #525: loss=0.19922487437725067
Epoch #526: loss=1.2205536365509033
Epoch #527: loss=1.5115511417388916
Epoch #528: loss=2.428103446960449
Epoch #529: loss=2.0455873012542725
Epoch #530: loss=0.39900651574134827
Epoch #531: loss=0.8724826574325562
Epoch #532: loss=1.5888872146606445
Epoch #533: loss=1.649641513824463
Epoch #534: loss=2.0126655101776123
Epoch #535: loss=1.3822548389434814
Epoch #536: loss=0.11757534742355347
Epoch #537: loss=0.33485129475593567
Epoch #538: loss=0.4028525650501251
Epoch #539: loss=1.3914189338684082
Epoch #540: loss=0.16738268733024597
Epoch #541: loss=1.7885738611221313
Epoch #542: loss=2.4471893310546875
Epoch #543: loss=0.20510712265968323
Epoch #544: loss=0.7428438663482666
Epoch #545: loss=0.7860126495361328
Epoch #546: loss=0.21597333252429962
Epoch #547: loss=1.6265685558319092
Epoch #548: loss=1.0485873222351074
Epoch #549: loss=0.8341190218925476
Epoch #550: loss=0.272211492061615
Epoch #551: loss=0.10027693212032318
Epoch #552: loss=0.3025144636631012
Epoch #553: loss=1.2919621467590332
Epoch #554: loss=1.478575587272644
Epoch #555: loss=0.4625180661678314
Epoch #556: loss=0.2531033158302307
Epoch #557: loss=1.186411738395691
Epoch #558: loss=0.34371235966682434
Epoch #559: loss=2.0422630310058594
Epoch #560: loss=1.2156636714935303
Epoch #561: loss=0.1163695901632309
Epoch #562: loss=0.21535605192184448
Epoch #563: loss=1.688009262084961
Epoch #564: loss=0.2410202920436859
Epoch #565: loss=0.19677792489528656
Epoch #566: loss=0.08230407536029816
Epoch #567: loss=0.9314576387405396
Epoch #568: loss=1.5518062114715576
Epoch #569: loss=0.20737653970718384
Epoch #570: loss=0.20116887986660004
Epoch #571: loss=0.6812450885772705
Epoch #572: loss=0.8107600212097168
Epoch #573: loss=1.3217594623565674
Epoch #574: loss=0.8787587881088257
Epoch #575: loss=0.6898106336593628
Epoch #576: loss=0.14068201184272766
Epoch #577: loss=0.663821816444397
Epoch #578: loss=0.16426198184490204
Epoch #579: loss=0.36956045031547546
Epoch #580: loss=0.4862251877784729
Epoch #581: loss=0.5103197693824768
Epoch #582: loss=0.5518488883972168
Epoch #583: loss=0.35720184445381165
Epoch #584: loss=1.505527377128601
Epoch #585: loss=1.8244110345840454
Epoch #586: loss=0.11126503348350525
Epoch #587: loss=1.7078906297683716
Epoch #588: loss=0.23770712316036224
Epoch #589: loss=0.2476593255996704
Epoch #590: loss=1.2943475246429443
Epoch #591: loss=1.923711895942688
Epoch #592: loss=0.24022671580314636
Epoch #593: loss=0.06407451629638672
Epoch #594: loss=1.6884044408798218
Epoch #595: loss=2.4225800037384033
Epoch #596: loss=0.17829643189907074
Epoch #597: loss=1.7280824184417725
Epoch #598: loss=0.14269526302814484
Epoch #599: loss=0.1525946408510208

Training time: 0:04:27.722521

Evaluation result: {'ours': {24: {'norm': {'MSE': 0.09093447823885772, 'MAE': 0.2444660961727188}, 'raw': {'MSE': 7.6516433978153415, 'MAE': 2.2424961760567976}}, 48: {'norm': {'MSE': 0.10627541329647779, 'MAE': 0.26504857133833426}, 'raw': {'MSE': 8.942499887810168, 'MAE': 2.4312999465799447}}, 96: {'norm': {'MSE': 0.1292584518805571, 'MAE': 0.2946752542287619}, 'raw': {'MSE': 10.876397946808652, 'MAE': 2.703066553851147}}, 288: {'norm': {'MSE': 0.18572878219363853, 'MAE': 0.35021772408948065}, 'raw': {'MSE': 15.628069998678034, 'MAE': 3.212559591065719}}, 672: {'norm': {'MSE': 0.22958746820824477, 'MAE': 0.3924864307918543}, 'raw': {'MSE': 19.318540559490394, 'MAE': 3.6002919273747547}}}, 'encoder_infer_time': 17.240046739578247, 'lr_train_time': {24: 3.2216343879699707, 48: 3.623534679412842, 96: 3.706763982772827, 288: 4.991279602050781, 672: 6.95696759223938}, 'lr_infer_time': {24: 0.027762889862060547, 48: 0.029367446899414062, 96: 0.04609060287475586, 288: 0.0750274658203125, 672: 0.1150655746459961}}
Finished.