Commit 4d736898 authored by Ard Kastrati's avatar Ard Kastrati
Browse files

Added the results for CNN without shuffling (true cross subject)

parent c8634d7a
......@@ -115,7 +115,7 @@ class ConvNet(ABC):
# early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=20)
ckpt_dir = config['model_dir'] + '/' + config['model'] + '_' + 'best_model.h5'
ckpt = tf.keras.callbacks.ModelCheckpoint(ckpt_dir, verbose=1, monitor='val_accuracy', save_best_only=True, mode='auto')
X_train, X_val, y_train, y_val = train_test_split(x, y, test_size=0.2, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(x, y, test_size=0.199182, shuffle=False)
prediction_ensemble = prediction_history((X_val,y_val))
hist = self.model.fit(X_train, y_train, verbose=2, batch_size=self.batch_size, validation_data=(X_val,y_val),
epochs=self.epochs, callbacks=[csv_logger, ckpt, prediction_ensemble])
......
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32;0.9746966361999512;0.06682214140892029;0.8877338767051697;0.4376406669616699
33;0.9785920977592468;0.05597871169447899;0.8587664365768433;0.42035749554634094
34;0.97611004114151;0.06395216286182404;0.8845461010932922;0.39000850915908813
35;0.9828323125839233;0.04644591361284256;0.8659736514091492;0.43734776973724365
36;0.9782474040985107;0.05798697844147682;0.8766458630561829;0.45011019706726074
37;0.9806950092315674;0.05335059016942978;0.8748440742492676;0.42973455786705017
38;0.9826944470405579;0.045531265437603;0.5668745636940002;1.4931753873825073
39;0.9821773171424866;0.04628022760152817;0.849341630935669;0.673201858997345
40;0.9832804799079895;0.044798657298088074;0.8361746072769165;0.6446897387504578
41;0.9829357266426086;0.046237390488386154;0.8358974456787109;0.6032375693321228
42;0.985659122467041;0.03882763534784317;0.8529452681541443;0.4517470896244049
43;0.9841078519821167;0.04355267435312271;0.8151074051856995;0.5961858630180359
44;0.983797550201416;0.04123908653855324;0.8371448516845703;0.5696343779563904
45;0.9852109551429749;0.038340672850608826;0.8777546882629395;0.4923771023750305
46;0.9848662614822388;0.04101666435599327;0.634511411190033;1.7668906450271606
47;0.9845559597015381;0.04204660281538963;0.8851004838943481;0.45032554864883423
48;0.98900306224823;0.030192691832780838;0.7276507019996643;1.0282957553863525
49;0.9840733408927917;0.04226312041282654;0.8795564770698547;0.44000956416130066
0;0.605901837348938;0.65091872215271;0.682328462600708;0.7627917528152466
1;0.7451393008232117;0.5022376775741577;0.7143450975418091;0.6954605579376221
2;0.8144994378089905;0.39979565143585205;0.8077616095542908;0.4157979488372803
3;0.8389754295349121;0.3561651110649109;0.7678447961807251;0.5056557655334473
4;0.8586941361427307;0.3220594525337219;0.7958419919013977;0.45203453302383423
5;0.8722766041755676;0.28742745518684387;0.7155925035476685;0.5821797847747803
6;0.8845146298408508;0.26396888494491577;0.8232848048210144;0.39673200249671936
7;0.8992002010345459;0.23847313225269318;0.7629937529563904;0.5109524726867676
8;0.9068877696990967;0.21977323293685913;0.8533610701560974;0.3758860230445862
9;0.9147476553916931;0.20100755989551544;0.8558558821678162;0.34342920780181885
10;0.9238141179084778;0.18468651175498962;0.8584892749786377;0.32616010308265686
11;0.9273648858070374;0.17285943031311035;0.867914080619812;0.33944520354270935
12;0.9328805804252625;0.16336214542388916;0.8734580874443054;0.31360331177711487
13;0.9405336380004883;0.1471981704235077;0.8238392472267151;0.5125515460968018
14;0.9429122805595398;0.13950444757938385;0.6720720529556274;1.1131342649459839
15;0.9471870064735413;0.12968863546848297;0.8256410360336304;0.4944644570350647
16;0.9525303244590759;0.11980130523443222;0.8877338767051697;0.31983011960983276
17;0.955529510974884;0.11025048047304153;0.8573804497718811;0.37932372093200684
18;0.9546676874160767;0.10945265740156174;0.8637560606002808;0.4315546154975891
19;0.9602178931236267;0.10112910717725754;0.8424116373062134;0.44267868995666504
20;0.96321702003479;0.09262143075466156;0.7323631048202515;0.9144607186317444
21;0.9664919972419739;0.08611100167036057;0.7244629263877869;0.6931208372116089
22;0.9672159552574158;0.08143900334835052;0.8350658416748047;0.561437726020813
23;0.9687327742576599;0.07932481914758682;0.757449746131897;0.6216828227043152
24;0.968422532081604;0.0770450085401535;0.8767845034599304;0.406706303358078
25;0.9736968874931335;0.07159489393234253;0.8576576709747314;0.4339984059333801
26;0.9731453657150269;0.07105682790279388;0.7460845708847046;0.5895518660545349
27;0.97428297996521;0.06605075299739838;0.7909910082817078;0.6439701914787292
28;0.9790058135986328;0.056600186973810196;0.8887040615081787;0.3949870765209198
29;0.9773855209350586;0.056925442069768906;0.871240496635437;0.6090938448905945
30;0.9789713025093079;0.05559932813048363;0.8936936855316162;0.3698657155036926
31;0.9789713025093079;0.05543547496199608;0.8011087775230408;0.5356136560440063
32;0.9818670749664307;0.048241689801216125;0.8670824766159058;0.4701766073703766
33;0.9806950092315674;0.05075034871697426;0.8386694192886353;0.6516269445419312
34;0.9809707403182983;0.05197061225771904;0.6892585158348083;0.8752534985542297
35;0.9821428656578064;0.04551886394619942;0.5812889933586121;1.5909043550491333
36;0.9826944470405579;0.0456695519387722;0.8625086545944214;0.5674229264259338
37;0.9847628474235535;0.038949448615312576;0.7241857051849365;1.0254591703414917
38;0.9849351644515991;0.04135515168309212;0.8619542717933655;0.6366874575614929
39;0.9821083545684814;0.04858926311135292;0.8911988735198975;0.38418254256248474
40;0.9881756901741028;0.03162972629070282;0.8706860542297363;0.49465593695640564
41;0.9826254844665527;0.04509063437581062;0.638808012008667;1.3604941368103027
42;0.9856246709823608;0.04067927598953247;0.7376299500465393;0.8855963945388794
43;0.9872793555259705;0.03401845321059227;0.8396396636962891;0.559405505657196
44;0.9848662614822388;0.04069497063755989;0.8834372758865356;0.4948660731315613
45;0.9857970476150513;0.03934117779135704;0.8790020942687988;0.5245204567909241
46;0.9892443418502808;0.02942851558327675;0.8752598762512207;0.5118622183799744
47;0.9882445931434631;0.03245730698108673;0.693277895450592;1.2650752067565918
48;0.986038327217102;0.03673391044139862;0.8744282722473145;0.5726521611213684
49;0.989382266998291;0.03078307956457138;0.8677754402160645;0.5213136672973633
best_model_train_loss,best_model_val_loss,best_model_train_acc,best_model_val_acc
0.056600186973810196,0.2665606141090393,0.9790058135986328,0.9002079002079002
loss,accuracy,val_loss,val_accuracy
0.65091872215271,0.605901837348938,0.4958110749721527,0.7586971586971587
0.5022376775741577,0.7451393008232117,0.4418316185474396,0.7916839916839917
0.39979565143585205,0.8144994378089905,0.4066786468029022,0.8138600138600138
0.3561651110649109,0.8389754295349121,0.409951388835907,0.8158004158004158
0.3220594525337219,0.8586941361427307,0.38301265239715576,0.8338184338184338
0.28742745518684387,0.8722766041755676,0.36084964871406555,0.8476784476784477
0.26396888494491577,0.8845146298408508,0.362216979265213,0.8442134442134442
0.23847313225269318,0.8992002010345459,0.3591266870498657,0.8479556479556479
0.21977323293685913,0.9068877696990967,0.3111116886138916,0.8783090783090783
0.20100755989551544,0.9147476553916931,0.3478747010231018,0.8753984753984754
0.18468651175498962,0.9238141179084778,0.33139699697494507,0.8688842688842688
0.17285943031311035,0.9273648858070374,0.2935536503791809,0.8819126819126819
0.16336214542388916,0.9328805804252625,0.2835180163383484,0.8877338877338877
0.1471981704235077,0.9405336380004883,0.29200461506843567,0.8842688842688843
0.13950444757938385,0.9429122805595398,0.34398770332336426,0.8572418572418572
0.12968863546848297,0.9471870064735413,0.30787014961242676,0.8742896742896743
0.11980130523443222,0.9525303244590759,0.2797829508781433,0.8874566874566875
0.11025048047304153,0.955529510974884,0.2997729778289795,0.8902286902286902
0.10945265740156174,0.9546676874160767,0.28516465425491333,0.8911988911988912
0.10112910717725754,0.9602178931236267,0.32762351632118225,0.8763686763686763
0.09262143075466156,0.96321702003479,0.35333892703056335,0.8508662508662509
0.08611100167036057,0.9664919972419739,0.3577347993850708,0.8690228690228691
0.08143900334835052,0.9672159552574158,0.3136979043483734,0.8717948717948718
0.07932481914758682,0.9687327742576599,0.3202773630619049,0.8798336798336799
0.0770450085401535,0.968422532081604,0.32105252146720886,0.868052668052668
0.07159489393234253,0.9736968874931335,0.2596580982208252,0.8952182952182952
0.07105682790279388,0.9731453657150269,0.3154726028442383,0.8740124740124741
0.06605075299739838,0.97428297996521,0.34460514783859253,0.8637560637560637
0.056600186973810196,0.9790058135986328,0.2665606141090393,0.9002079002079002
0.056925442069768906,0.9773855209350586,0.3126254379749298,0.8766458766458767
0.05559932813048363,0.9789713025093079,0.2787521779537201,0.8855162855162855
0.05543547496199608,0.9789713025093079,0.2939371168613434,0.8891198891198892
0.048241689801216125,0.9818670749664307,0.32680442929267883,0.8722106722106722
0.05075034871697426,0.9806950092315674,0.2721937894821167,0.8917532917532918
0.05197061225771904,0.9809707403182983,0.29907411336898804,0.8906444906444907
0.04551886394619942,0.9821428656578064,0.34950196743011475,0.8772002772002772
0.0456695519387722,0.9826944470405579,0.31809863448143005,0.8766458766458767
0.038949448615312576,0.9847628474235535,0.2813093960285187,0.896049896049896
0.04135515168309212,0.9849351644515991,0.34884971380233765,0.8794178794178794
0.04858926311135292,0.9821083545684814,0.2790725529193878,0.8852390852390852
0.03162972629070282,0.9881756901741028,0.2728796899318695,0.8950796950796951
0.04509063437581062,0.9826254844665527,0.34052738547325134,0.8602910602910603
0.04067927598953247,0.9856246709823608,0.34273141622543335,0.8584892584892585
0.03401845321059227,0.9872793555259705,0.3219185173511505,0.8738738738738738
0.04069497063755989,0.9848662614822388,0.2770305573940277,0.896049896049896
0.03934117779135704,0.9857970476150513,0.28747519850730896,0.8977130977130977
0.02942851558327675,0.9892443418502808,0.3216732442378998,0.887040887040887
0.03245730698108673,0.9882445931434631,0.33085423707962036,0.8683298683298684
0.03673391044139862,0.986038327217102,0.26644769310951233,0.8985446985446985
0.03078307956457138,0.989382266998291,0.26229792833328247,0.8972972972972973
INFO:root:Started the Logging
INFO:root:X training loaded.
INFO:root:(129, 500, 36223)
INFO:root:y training loaded.
INFO:root:(1, 36223)
INFO:root:Setting the shapes
INFO:root:(36223, 500, 129)
INFO:root:(36223, 1)
INFO:root:Started running cnn. If you want to run other methods please choose another model in the config.py file.
INFO:root:Parameters:
INFO:root:--------------- use residual : True
INFO:root:--------------- depth : 12
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 64
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : True
INFO:root:--------------- depth : 12
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 64
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : True
INFO:root:--------------- depth : 12
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 64
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : True
INFO:root:--------------- depth : 12
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 64
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : True
INFO:root:--------------- depth : 12
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 64
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:**********
INFO:root:--- Runtime: 4655.170459032059 seconds ---
INFO:root:Finished Logging
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