Commit 22f84c06 authored by PolinaFrost's avatar PolinaFrost
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# BachelorThesis
Bachelor thesis "A Comparison Of Temporal Pooling Methods for 3D MRI scans of brain with skull-stripping converted to 2D Convolutional Neural Networks" by Polina Moroza
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Dynamic pooling, early fusion, 4:1, weighted binary cross entropy loss, pooling dimension X
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TRAINING FINISHED
==========================
Training time: 1994.724234342575 seconds
EVALUATION ON TEST DATASET
==========================
Accuracy: 0.5789473684210527
Area under the ROC-curve: 0.5710144927536231
Labels: tensor([1., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 1.,
0., 1., 0., 0., 0., 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.,
1., 0.])
(final) Predictions: tensor([1., 1., 0., 0., 0., 0., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 1., 0.,
0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0.,
1., 0.])
Dynamic pooling, early fusion, 4:1, weighted binary cross entropy loss, pooling dimension X
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[88/100] train-loss: 0.24546797648072244 train-acc: 0.9125 train-auROC: 0.9066666666666667 time: 21.650837898254395
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[90/100] train-loss: 0.21110221073031427 train-acc: 0.9875 train-auROC: 0.988235294117647 time: 20.859002113342285
[91/100] train-loss: 0.1930603165179491 train-acc: 0.98125 train-auROC: 0.9823529411764707 time: 17.36600947380066
[92/100] train-loss: 0.18869729787111283 train-acc: 0.99375 train-auROC: 0.9941176470588236 time: 19.950753688812256
[93/100] train-loss: 0.19970555379986762 train-acc: 0.9875 train-auROC: 0.988235294117647 time: 19.09560203552246
[94/100] train-loss: 0.19153751693665982 train-acc: 0.96875 train-auROC: 0.9705882352941176 time: 18.7507586479187
[95/100] train-loss: 0.2067329004406929 train-acc: 0.98125 train-auROC: 0.9823529411764707 time: 18.31478500366211
[96/100] train-loss: 0.19577793702483176 train-acc: 0.94375 train-auROC: 0.9470588235294118 time: 16.899369716644287
[97/100] train-loss: 0.1865692175924778 train-acc: 0.98125 train-auROC: 0.9823529411764707 time: 19.202929496765137
[98/100] train-loss: 0.18176691308617593 train-acc: 0.9875 train-auROC: 0.988235294117647 time: 19.143887042999268
[99/100] train-loss: 0.1627547949552536 train-acc: 0.9875 train-auROC: 0.988235294117647 time: 19.883852243423462
[100/100] train-loss: 0.17189031466841698 train-acc: 0.96875 train-auROC: 0.9705882352941176 time: 18.720346212387085
TRAINING FINISHED
==========================
Training time: 2037.142686843872 seconds
EVALUATION ON TEST DATASET
==========================
Accuracy: 0.4473684210526316
Area under the ROC-curve: 0.4507246376811595
Labels: tensor([1., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 1.,
0., 1., 0., 0., 0., 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.,
1., 0.])
(final) Predictions: tensor([1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 0., 1., 0.,
1., 0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 1., 0.,
0., 0.])
Dynamic pooling, early fusion, 4:1, weighted binary cross entropy loss, pooling dimension X
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[95/100] train-loss: 0.18041021097451448 train-acc: 0.99375 train-auROC: 0.9941176470588236 time: 19.359951734542847
[96/100] train-loss: 0.16569313127547503 train-acc: 0.975 train-auROC: 0.9764705882352941 time: 20.124733686447144
[97/100] train-loss: 0.18114127032458782 train-acc: 0.9625 train-auROC: 0.9647058823529412 time: 19.990232467651367
[98/100] train-loss: 0.17725905925035476 train-acc: 0.975 train-auROC: 0.9764705882352941 time: 18.437843561172485
[99/100] train-loss: 0.16219991408288478 train-acc: 0.98125 train-auROC: 0.9815686274509805 time: 18.778470039367676
[100/100] train-loss: 0.182870533131063 train-acc: 0.89375 train-auROC: 0.9 time: 20.29149293899536
TRAINING FINISHED
==========================
Training time: 2010.34801197052 seconds
EVALUATION ON TEST DATASET
==========================
Accuracy: 0.3684210526315789
Area under the ROC-curve: 0.408695652173913
Labels: tensor([1., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 1.,
0., 1., 0., 0., 0., 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.,
1., 0.])
(final) Predictions: tensor([1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 0.,
0., 0., 1., 1., 1., 0., 0., 0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1.,
1., 1.])
Dynamic pooling, late fusion, 4:1, weighted binary cross entropy loss, pooling dimension X
[1/100] train-loss: 1.4880589067935943 train-acc: 0.46875 train-auROC: 0.5 time: 14.937860012054443
[2/100] train-loss: 1.31999970972538 train-acc: 0.79375 train-auROC: 0.7909803921568628 time: 13.998232126235962
[3/100] train-loss: 1.1314771085977555 train-acc: 0.675 train-auROC: 0.6831372549019609 time: 14.952365398406982
[4/100] train-loss: 1.0025690197944641 train-acc: 0.6125 train-auROC: 0.632156862745098 time: 16.6370108127594
[5/100] train-loss: 0.8943002119660377 train-acc: 0.575 train-auROC: 0.5984313725490196 time: 17.390396118164062
[6/100] train-loss: 1.020717027783394 train-acc: 0.5875 train-auROC: 0.6094117647058823 time: 16.11204504966736
[7/100] train-loss: 0.8285294517874717 train-acc: 0.7125 train-auROC: 0.7184313725490197 time: 15.793324947357178
[8/100] train-loss: 0.7862752869725227 train-acc: 0.8 train-auROC: 0.7960784313725491 time: 16.423004150390625
[9/100] train-loss: 0.8126973912119866 train-acc: 0.80625 train-auROC: 0.8027450980392157 time: 16.056057929992676
[10/100] train-loss: 0.782758255302906 train-acc: 0.7625 train-auROC: 0.7537254901960785 time: 16.186466455459595
[11/100] train-loss: 0.8123928710818291 train-acc: 0.8 train-auROC: 0.7952941176470589 time: 16.046420335769653
[12/100] train-loss: 0.7479313403367996 train-acc: 0.7875 train-auROC: 0.7803921568627451 time: 16.06964612007141
[13/100] train-loss: 0.6826961800456047 train-acc: 0.78125 train-auROC: 0.7745098039215685 time: 16.006600856781006
[14/100] train-loss: 0.6692001119256019 train-acc: 0.6875 train-auROC: 0.6988235294117646 time: 15.595229864120483
[15/100] train-loss: 0.649668337404728 train-acc: 0.76875 train-auROC: 0.7729411764705881 time: 15.827821731567383
[16/100] train-loss: 0.6043287754058838 train-acc: 0.8 train-auROC: 0.7992156862745098 time: 16.02165174484253
[17/100] train-loss: 0.6003746315836906 train-acc: 0.8 train-auROC: 0.7952941176470589 time: 16.14517593383789
[18/100] train-loss: 0.5505035862326622 train-acc: 0.78125 train-auROC: 0.7847058823529413 time: 16.202608585357666
[19/100] train-loss: 0.5845268368721008 train-acc: 0.8125 train-auROC: 0.8086274509803921 time: 16.358580589294434
[20/100] train-loss: 0.6223260328173638 train-acc: 0.8 train-auROC: 0.7945098039215686 time: 15.77730107307434
[21/100] train-loss: 0.5777749374508858 train-acc: 0.8125 train-auROC: 0.8125490196078432 time: 15.343868494033813
[22/100] train-loss: 0.5537071391940117 train-acc: 0.8 train-auROC: 0.7937254901960784 time: 16.31200385093689
[23/100] train-loss: 0.6078408822417259 train-acc: 0.825 train-auROC: 0.8211764705882353 time: 16.14492678642273
[24/100] train-loss: 0.5446707829833031 train-acc: 0.8125 train-auROC: 0.8086274509803921 time: 15.58030652999878
[25/100] train-loss: 0.5138420552015305 train-acc: 0.76875 train-auROC: 0.7737254901960785 time: 14.936944007873535
[26/100] train-loss: 0.5404691115021706 train-acc: 0.75625 train-auROC: 0.7619607843137256 time: 15.325276851654053
[27/100] train-loss: 0.5917637705802917 train-acc: 0.725 train-auROC: 0.7333333333333333 time: 15.272620916366577
[28/100] train-loss: 0.5548051983118057 train-acc: 0.8125 train-auROC: 0.8117647058823529 time: 15.881844282150269
[29/100] train-loss: 0.5373675927519799 train-acc: 0.65625 train-auROC: 0.6725490196078432 time: 16.38326597213745
[30/100] train-loss: 0.5423107430338859 train-acc: 0.78125 train-auROC: 0.771372549019608 time: 16.54951763153076
[31/100] train-loss: 0.5472118005156517 train-acc: 0.81875 train-auROC: 0.8152941176470588 time: 16.64138889312744
[32/100] train-loss: 0.5306306213140488 train-acc: 0.8125 train-auROC: 0.8086274509803921 time: 16.03804111480713
[33/100] train-loss: 0.5133487015962601 train-acc: 0.83125 train-auROC: 0.8286274509803921 time: 16.146705627441406
[34/100] train-loss: 0.5468390047550201 train-acc: 0.825 train-auROC: 0.8235294117647058 time: 15.78539490699768
[35/100] train-loss: 0.500006540119648 train-acc: 0.825 train-auROC: 0.8227450980392157 time: 15.629241228103638
[36/100] train-loss: 0.5283960610628128 train-acc: 0.8 train-auROC: 0.7929411764705884 time: 16.127100706100464
[37/100] train-loss: 0.4974818155169487 train-acc: 0.75625 train-auROC: 0.7627450980392156 time: 15.911651372909546
[38/100] train-loss: 0.5210612624883652 train-acc: 0.8375 train-auROC: 0.8352941176470589 time: 15.95057988166809
[39/100] train-loss: 0.4835494920611382 train-acc: 0.8375 train-auROC: 0.8337254901960786 time: 16.13302516937256
[40/100] train-loss: 0.49525319784879684 train-acc: 0.8375 train-auROC: 0.8376470588235294 time: 16.363762617111206
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[47/100] train-loss: 0.5055233150720596 train-acc: 0.84375 train-auROC: 0.8443137254901962 time: 16.641546964645386
[48/100] train-loss: 0.46288315653800965 train-acc: 0.84375 train-auROC: 0.839607843137255 time: 16.654688358306885
[49/100] train-loss: 0.475981405377388 train-acc: 0.8625 train-auROC: 0.8619607843137256 time: 16.267869234085083
[50/100] train-loss: 0.49415656849741935 train-acc: 0.85 train-auROC: 0.8470588235294119 time: 15.762482643127441
[51/100] train-loss: 0.4802910327911377 train-acc: 0.825 train-auROC: 0.8188235294117646 time: 15.262491464614868
[52/100] train-loss: 0.45376132503151895 train-acc: 0.84375 train-auROC: 0.8450980392156862 time: 15.80391550064087
[53/100] train-loss: 0.46546672135591505 train-acc: 0.83125 train-auROC: 0.8247058823529412 time: 15.883477449417114
[54/100] train-loss: 0.4772694431245327 train-acc: 0.79375 train-auROC: 0.7996078431372549 time: 16.64199995994568
[55/100] train-loss: 0.47257903665304185 train-acc: 0.85625 train-auROC: 0.8529411764705882 time: 15.66354775428772
[56/100] train-loss: 0.47698690593242643 train-acc: 0.8375 train-auROC: 0.8305882352941175 time: 15.781195163726807
[57/100] train-loss: 0.45365589633584025 train-acc: 0.83125 train-auROC: 0.8325490196078432 time: 15.561507225036621
[58/100] train-loss: 0.46277627497911455 train-acc: 0.83125 train-auROC: 0.8333333333333334 time: 15.865702152252197
[59/100] train-loss: 0.46544126272201536 train-acc: 0.85 train-auROC: 0.8494117647058823 time: 15.459567308425903
[60/100] train-loss: 0.47038191854953765 train-acc: 0.8625 train-auROC: 0.8619607843137256 time: 15.943006992340088
[61/100] train-loss: 0.47309140488505363 train-acc: 0.8125 train-auROC: 0.8156862745098039 time: 15.907444715499878
[62/100] train-loss: 0.4443289957940578 train-acc: 0.83125 train-auROC: 0.8341176470588235 time: 15.8959481716156
[63/100] train-loss: 0.4798664689064026 train-acc: 0.84375 train-auROC: 0.8450980392156862 time: 16.40747570991516
[64/100] train-loss: 0.4370876088738441 train-acc: 0.85625 train-auROC: 0.8529411764705882 time: 15.619679689407349
[65/100] train-loss: 0.44165501892566683 train-acc: 0.85625 train-auROC: 0.8513725490196079 time: 16.389381408691406
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[73/100] train-loss: 0.43997414112091066 train-acc: 0.8875 train-auROC: 0.8862745098039215 time: 16.29643177986145
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[77/100] train-loss: 0.3848813556134701 train-acc: 0.86875 train-auROC: 0.8701960784313726 time: 16.179943561553955
[78/100] train-loss: 0.4088097125291824 train-acc: 0.86875 train-auROC: 0.8701960784313726 time: 17.081061601638794
[79/100] train-loss: 0.42639519795775416 train-acc: 0.85625 train-auROC: 0.8568627450980393 time: 16.26651644706726
[80/100] train-loss: 0.4228969603776932 train-acc: 0.8625 train-auROC: 0.8650980392156862 time: 16.45558738708496
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[83/100] train-loss: 0.39305343106389046 train-acc: 0.88125 train-auROC: 0.8803921568627452 time: 16.06123685836792
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[85/100] train-loss: 0.3962516948580742 train-acc: 0.88125 train-auROC: 0.8788235294117646 time: 16.15265989303589
[86/100] train-loss: 0.38023336976766586 train-acc: 0.8375 train-auROC: 0.8423529411764705 time: 16.195049047470093
[87/100] train-loss: 0.420276403427124 train-acc: 0.875 train-auROC: 0.8768627450980393 time: 16.43251943588257
[88/100] train-loss: 0.388942401856184 train-acc: 0.88125 train-auROC: 0.8811764705882352 time: 16.544649839401245
[89/100] train-loss: 0.38365106359124185 train-acc: 0.89375 train-auROC: 0.8905882352941176 time: 16.233561038970947
[90/100] train-loss: 0.37723111137747767 train-acc: 0.88125 train-auROC: 0.8796078431372549 time: 16.30724287033081
[91/100] train-loss: 0.4162517748773098 train-acc: 0.90625 train-auROC: 0.9054901960784313 time: 16.3054358959198
[92/100] train-loss: 0.4074863359332085 train-acc: 0.8625 train-auROC: 0.8564705882352941 time: 16.181702375411987
[93/100] train-loss: 0.3973125070333481 train-acc: 0.89375 train-auROC: 0.8937254901960785 time: 15.367408990859985
[94/100] train-loss: 0.37517542466521264 train-acc: 0.9 train-auROC: 0.9003921568627452 time: 16.17254066467285
[95/100] train-loss: 0.3653267413377762 train-acc: 0.9 train-auROC: 0.9003921568627452 time: 16.128554344177246
[96/100] train-loss: 0.40356930419802667 train-acc: 0.9 train-auROC: 0.8972549019607844 time: 15.81200647354126
[97/100] train-loss: 0.37350698187947273 train-acc: 0.8875 train-auROC: 0.8878431372549018 time: 16.537153482437134
[98/100] train-loss: 0.3680443800985813 train-acc: 0.9 train-auROC: 0.9003921568627452 time: 16.68929672241211
[99/100] train-loss: 0.3736072085797787 train-acc: 0.89375 train-auROC: 0.891372549019608 time: 15.984046459197998
[100/100] train-loss: 0.3480771526694298 train-acc: 0.83125 train-auROC: 0.8364705882352941 time: 15.542993545532227
TRAINING FINISHED
==========================
Training time: 1678.058821439743 seconds
EVALUATION ON TEST DATASET
==========================
Accuracy: 0.631578947368421
Area under the ROC-curve: 0.6608695652173913
Labels: tensor([1., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 1.,
0., 1., 0., 0., 0., 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.,
1., 0.])
(final) Predictions: tensor([0., 1., 1., 1., 0., 1., 0., 1., 0., 1., 1., 1., 0., 0., 1., 0., 1., 1.,
0., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1., 1., 1., 1., 0., 1., 1., 0.,
1., 0.])
Dynamic pooling, late fusion, 4:1, weighted binary cross entropy loss, pooling dimension X
[1/100] train-loss: 1.3202808856964112 train-acc: 0.46875 train-auROC: 0.5 time: 16.005027294158936
[2/100] train-loss: 1.244609570503235 train-acc: 0.66875 train-auROC: 0.679607843137255 time: 16.072606325149536
[3/100] train-loss: 1.0620848566293717 train-acc: 0.7375 train-auROC: 0.723921568627451 time: 16.161222219467163
[4/100] train-loss: 0.9948337972164154 train-acc: 0.725 train-auROC: 0.7301960784313726 time: 17.459638357162476
[5/100] train-loss: 0.8587361752986908 train-acc: 0.625 train-auROC: 0.64 time: 16.6501407623291
[6/100] train-loss: 0.7116939723491669 train-acc: 0.78125 train-auROC: 0.7729411764705884 time: 16.096875190734863
[7/100] train-loss: 0.8201918587088585 train-acc: 0.8125 train-auROC: 0.807843137254902 time: 16.05911159515381
[8/100] train-loss: 0.8162078782916069 train-acc: 0.8 train-auROC: 0.7992156862745098 time: 15.917419195175171
[9/100] train-loss: 0.7247253075242043 train-acc: 0.725 train-auROC: 0.7317647058823529 time: 16.193599700927734
[10/100] train-loss: 0.7516102313995361 train-acc: 0.79375 train-auROC: 0.7956862745098039 time: 15.64084005355835
[11/100] train-loss: 0.7022775799036026 train-acc: 0.71875 train-auROC: 0.7250980392156863 time: 16.443313121795654
[12/100] train-loss: 0.654858173429966 train-acc: 0.79375 train-auROC: 0.7862745098039216 time: 15.77284860610962
[13/100] train-loss: 0.6289488792419433 train-acc: 0.81875 train-auROC: 0.8129411764705883 time: 16.31105136871338
[14/100] train-loss: 0.665584833920002 train-acc: 0.75625 train-auROC: 0.7454901960784314 time: 15.259845495223999
[15/100] train-loss: 0.6601523295044899 train-acc: 0.8125 train-auROC: 0.807843137254902 time: 16.678700923919678
[16/100] train-loss: 0.6217268198728562 train-acc: 0.825 train-auROC: 0.8219607843137255 time: 16.52560067176819
[17/100] train-loss: 0.5874111890792847 train-acc: 0.80625 train-auROC: 0.8058823529411765 time: 15.830601692199707
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[19/100] train-loss: 0.5806468293070793 train-acc: 0.80625 train-auROC: 0.8090196078431373 time: 16.06159234046936
[20/100] train-loss: 0.5563061714172364 train-acc: 0.75625 train-auROC: 0.7627450980392156 time: 15.98986554145813
[21/100] train-loss: 0.5570686787366868 train-acc: 0.7625 train-auROC: 0.7513725490196078 time: 16.180833339691162
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[24/100] train-loss: 0.5507216066122055 train-acc: 0.775 train-auROC: 0.7803921568627451 time: 16.023900508880615
[25/100] train-loss: 0.5641782522201538 train-acc: 0.83125 train-auROC: 0.8301960784313726 time: 15.69217324256897
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[38/100] train-loss: 0.4862214960157871 train-acc: 0.85625 train-auROC: 0.8560784313725491 time: 16.493450164794922
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[40/100] train-loss: 0.5101778343319893 train-acc: 0.825 train-auROC: 0.819607843137255 time: 16.533002138137817
[41/100] train-loss: 0.46426921635866164 train-acc: 0.81875 train-auROC: 0.8129411764705883 time: 16.314996242523193
[42/100] train-loss: 0.480065156519413 train-acc: 0.76875 train-auROC: 0.7752941176470588 time: 16.77931261062622
[43/100] train-loss: 0.49573512822389604 train-acc: 0.85 train-auROC: 0.8501960784313726 time: 16.200889348983765
[44/100] train-loss: 0.5149821132421494 train-acc: 0.79375 train-auROC: 0.7988235294117647 time: 16.311537742614746
[45/100] train-loss: 0.493854795396328 train-acc: 0.825 train-auROC: 0.828235294117647 time: 16.691397190093994
[46/100] train-loss: 0.4762954816222191 train-acc: 0.875 train-auROC: 0.8737254901960785 time: 15.90639352798462
[47/100] train-loss: 0.4617877513170242 train-acc: 0.85625 train-auROC: 0.8560784313725491 time: 16.298786163330078
[48/100] train-loss: 0.45217867791652677 train-acc: 0.85 train-auROC: 0.8509803921568628 time: 16.27497363090515
[49/100] train-loss: 0.4896311953663826 train-acc: 0.8 train-auROC: 0.8047058823529412 time: 16.672264099121094
[50/100] train-loss: 0.44995316341519354 train-acc: 0.88125 train-auROC: 0.8796078431372549 time: 15.992247343063354
[51/100] train-loss: 0.4695526257157326 train-acc: 0.8625 train-auROC: 0.8619607843137256 time: 16.10443139076233
[52/100] train-loss: 0.47968495860695837 train-acc: 0.78125 train-auROC: 0.7901960784313724 time: 16.37518620491028
[53/100] train-loss: 0.45938916206359864 train-acc: 0.7875 train-auROC: 0.7937254901960784 time: 16.804446697235107
[54/100] train-loss: 0.47391909211874006 train-acc: 0.825 train-auROC: 0.828235294117647 time: 16.221890211105347
[55/100] train-loss: 0.44660009294748304 train-acc: 0.84375 train-auROC: 0.839607843137255 time: 16.307499885559082
[56/100] train-loss: 0.44707081466913223 train-acc: 0.85 train-auROC: 0.8462745098039216 time: 16.442938566207886
[57/100] train-loss: 0.44158354848623277 train-acc: 0.88125 train-auROC: 0.8796078431372549 time: 17.17974615097046
[58/100] train-loss: 0.4551699683070183 train-acc: 0.86875 train-auROC: 0.8662745098039216 time: 16.45927405357361
[59/100] train-loss: 0.4413180038332939 train-acc: 0.84375 train-auROC: 0.8380392156862745 time: 15.679457187652588
[60/100] train-loss: 0.42706566751003266 train-acc: 0.85 train-auROC: 0.8509803921568628 time: 15.808072805404663
[61/100] train-loss: 0.4460426077246666 train-acc: 0.875 train-auROC: 0.8745098039215686 time: 16.441534757614136
[62/100] train-loss: 0.42622537910938263 train-acc: 0.80625 train-auROC: 0.8113725490196079 time: 15.91335391998291
[63/100] train-loss: 0.4596482694149017 train-acc: 0.85 train-auROC: 0.8462745098039216 time: 16.442854642868042
[64/100] train-loss: 0.48182443976402284 train-acc: 0.84375 train-auROC: 0.8372549019607843 time: 16.469990491867065
[65/100] train-loss: 0.43042216897010804 train-acc: 0.85 train-auROC: 0.8454901960784313 time: 16.25506567955017
[66/100] train-loss: 0.4443736381828785 train-acc: 0.88125 train-auROC: 0.8803921568627452 time: 17.13145089149475
[67/100] train-loss: 0.41543100625276563 train-acc: 0.85 train-auROC: 0.8525490196078431 time: 16.24681258201599
[68/100] train-loss: 0.4479183480143547 train-acc: 0.85625 train-auROC: 0.8584313725490196 time: 16.554903030395508
[69/100] train-loss: 0.41097277849912645 train-acc: 0.88125 train-auROC: 0.8811764705882352 time: 16.808964252471924
[70/100] train-loss: 0.43032386302948 train-acc: 0.875 train-auROC: 0.8721568627450981 time: 16.650387048721313
[71/100] train-loss: 0.41526473835110667 train-acc: 0.8625 train-auROC: 0.8588235294117647 time: 16.13554334640503
[72/100] train-loss: 0.44864932000637053 train-acc: 0.84375 train-auROC: 0.8380392156862745 time: 16.06103491783142
[73/100] train-loss: 0.4451553262770176 train-acc: 0.85625 train-auROC: 0.8513725490196079 time: 16.12229609489441
[74/100] train-loss: 0.41955858170986177 train-acc: 0.88125 train-auROC: 0.8788235294117646 time: 15.999131679534912
[75/100] train-loss: 0.4224513337016106 train-acc: 0.86875 train-auROC: 0.8654901960784315 time: 16.0516300201416
[76/100] train-loss: 0.3905831418931484 train-acc: 0.85625 train-auROC: 0.8490196078431373 time: 15.918561458587646
[77/100] train-loss: 0.41298349723219874 train-acc: 0.86875 train-auROC: 0.8709803921568628 time: 16.19863748550415
[78/100] train-loss: 0.4028587728738785 train-acc: 0.875 train-auROC: 0.876078431372549 time: 16.560638666152954
[79/100] train-loss: 0.41143329069018364 train-acc: 0.875 train-auROC: 0.8713725490196079 time: 16.242099285125732
[80/100] train-loss: 0.3746443271636963 train-acc: 0.8375 train-auROC: 0.8305882352941175 time: 15.860228776931763
[81/100] train-loss: 0.39125475808978083 train-acc: 0.85 train-auROC: 0.8423529411764705 time: 15.414232015609741
[82/100] train-loss: 0.40784209594130516 train-acc: 0.85625 train-auROC: 0.8513725490196079 time: 16.477962255477905
[83/100] train-loss: 0.4130022168159485 train-acc: 0.88125 train-auROC: 0.8780392156862745 time: 16.456339597702026
[84/100] train-loss: 0.3890870980918407 train-acc: 0.85625 train-auROC: 0.8607843137254902 time: 16.681987047195435
[85/100] train-loss: 0.41233403235673904 train-acc: 0.8875 train-auROC: 0.8878431372549018 time: 16.452845811843872
[86/100] train-loss: 0.3919442228972912 train-acc: 0.76875 train-auROC: 0.7784313725490196 time: 16.79211163520813
[87/100] train-loss: 0.3628571920096874 train-acc: 0.8 train-auROC: 0.807843137254902 time: 16.68706703186035
[88/100] train-loss: 0.3824707418680191 train-acc: 0.88125 train-auROC: 0.8819607843137255 time: 17.007434129714966
[89/100] train-loss: 0.39354642778635024 train-acc: 0.89375 train-auROC: 0.8945098039215686 time: 16.849992275238037
[90/100] train-loss: 0.3729206778109074 train-acc: 0.8875 train-auROC: 0.8886274509803922 time: 16.378106832504272
[91/100] train-loss: 0.3772688664495945 train-acc: 0.89375 train-auROC: 0.8937254901960785 time: 16.76032781600952
[92/100] train-loss: 0.42202667742967603 train-acc: 0.9 train-auROC: 0.8980392156862746 time: 16.115150451660156
[93/100] train-loss: 0.35434864535927774 train-acc: 0.88125 train-auROC: 0.8780392156862745 time: 16.657058477401733
[94/100] train-loss: 0.3770204983651638 train-acc: 0.85625 train-auROC: 0.8490196078431373 time: 16.845801830291748
[95/100] train-loss: 0.37649961933493614 train-acc: 0.88125 train-auROC: 0.8780392156862745 time: 16.601033926010132
[96/100] train-loss: 0.34947910383343694 train-acc: 0.8875 train-auROC: 0.8854901960784314 time: 15.960646629333496
[97/100] train-loss: 0.4068301886320114 train-acc: 0.8125 train-auROC: 0.8196078431372549 time: 16.169989347457886
[98/100] train-loss: 0.36682138219475746 train-acc: 0.9125 train-auROC: 0.912156862745098 time: 16.629958391189575
[99/100] train-loss: 0.34471865370869637 train-acc: 0.8875 train-auROC: 0.8823529411764707 time: 16.835829496383667
[100/100] train-loss: 0.3623536929488182 train-acc: 0.90625 train-auROC: 0.9062745098039215 time: 16.440685749053955
TRAINING FINISHED
==========================
Training time: 1707.6185021400452 seconds
EVALUATION ON TEST DATASET
==========================
Accuracy: 0.631578947368421
Area under the ROC-curve: 0.6260869565217391
Labels: tensor([1., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 1.,
0., 1., 0., 0., 0., 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.,
1., 0.])
(final) Predictions: tensor([0., 1., 1., 0., 0., 1., 0., 1., 0., 1., 1., 1., 0., 0., 1., 0., 1., 0.,
0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 1., 0., 1., 1., 0.,
1., 0.])
Dynamic pooling, late fusion, 4:1, weighted binary cross entropy loss, pooling dimension X
[1/100] train-loss: 1.3639574021100997 train-acc: 0.75 train-auROC: 0.7443137254901961 time: 15.369850158691406
[2/100] train-loss: 1.1283897891640664 train-acc: 0.46875 train-auROC: 0.5 time: 15.47080683708191
[3/100] train-loss: 1.128762599825859 train-acc: 0.64375 train-auROC: 0.6615686274509804 time: 15.882234573364258
[4/100] train-loss: 0.9227122485637664 train-acc: 0.68125 train-auROC: 0.6913725490196079 time: 16.498650074005127
[5/100] train-loss: 0.8799163267016411 train-acc: 0.8125 train-auROC: 0.8086274509803921 time: 15.458577871322632
[6/100] train-loss: 0.8986567616462707 train-acc: 0.68125 train-auROC: 0.692156862745098 time: 16.0930335521698
[7/100] train-loss: 0.8110610648989678 train-acc: 0.76875 train-auROC: 0.7698039215686274 time: 14.8385910987854
[8/100] train-loss: 0.8080090850591659 train-acc: 0.78125 train-auROC: 0.7737254901960785 time: 15.859803676605225
[9/100] train-loss: 0.7540892899036408 train-acc: 0.7625 train-auROC: 0.7521568627450981 time: 15.652067184448242
[10/100] train-loss: 0.7490985959768295 train-acc: 0.8 train-auROC: 0.7976470588235294 time: 15.590442419052124
[11/100] train-loss: 0.7149681270122528 train-acc: 0.775 train-auROC: 0.7788235294117647 time: 15.240765810012817
[12/100] train-loss: 0.6296735808253289 train-acc: 0.8 train-auROC: 0.8015686274509803 time: 15.758767366409302
[13/100] train-loss: 0.6559229254722595 train-acc: 0.775 train-auROC: 0.7788235294117647 time: 15.697863817214966
[14/100] train-loss: 0.6943721979856491 train-acc: 0.6375 train-auROC: 0.6541176470588235 time: 16.441173315048218
[15/100] train-loss: 0.6193204820156097 train-acc: 0.725 train-auROC: 0.7333333333333333 time: 15.37262511253357
[16/100] train-loss: 0.5852930650115014 train-acc: 0.80625 train-auROC: 0.8050980392156862 time: 16.167941331863403
[17/100] train-loss: 0.6436418414115905 train-acc: 0.8125 train-auROC: 0.8101960784313725 time: 15.223195791244507
[18/100] train-loss: 0.6525467962026597 train-acc: 0.80625 train-auROC: 0.8082352941176469 time: 16.2790629863739
[19/100] train-loss: 0.5803059667348862 train-acc: 0.7625 train-auROC: 0.7686274509803923 time: 16.41700291633606
[20/100] train-loss: 0.6056324809789657 train-acc: 0.725 train-auROC: 0.7349019607843137 time: 16.021934032440186
[21/100] train-loss: 0.6172055542469025 train-acc: 0.76875 train-auROC: 0.7745098039215685 time: 16.11131525039673
[22/100] train-loss: 0.5553556829690933 train-acc: 0.81875 train-auROC: 0.8152941176470588 time: 15.177433729171753
[23/100] train-loss: 0.597848404943943 train-acc: 0.80625 train-auROC: 0.8019607843137255 time: 16.39525532722473