Commit 907806f7 by Ard Kastrati

### Merge branch 'master' of https://gitlab.ethz.ch/kard/dl-project

parents 398c7dc9 1ffa2794
cluster2.py 0 → 100644
 import numpy as np def clustering(): c1=list(np.array([12,5,4,11,19,18,16,10,15,9,22])-1)#front c2=list(np.array([8,1,2,125,126,127,14,21,25,26,31,128])-1) #EOG c3=list(np.array([114,115,109,108,113,107,103,110,111,104,93,102,96,91,102,98,92,116])-1) #right c4=list(np.array([56,49,44,39,45,50,58,59,51,46,40,34,35,41,47,52,42,36])-1)#left c5=list(np.array([37,30,29,6,112,7,106,105,111,31,80,87,31,55,79,78,61,54])-1) #cent c6=list(np.array([67,77,72,66,71,76,70,75,83,69,74,82,89,68,94,73,88,69])-1) #occi cluster2={} cluster2['c1']=c1 cluster2['c2']=c2 cluster2['c3']=c3 cluster2['c4']=c4 cluster2['c5']=c5 cluster2['c6']=c6 return cluster2
cluster3.py 0 → 100644
 import numpy as np def clustering(): c1=list(np.array([15,18,16,10,11,12,5,6,7,106,31,80])-1)#mfront c2=list(np.array([55,54,79,78,77,76,75,71,67,61,72,62])-1) #mparietal c3=list(np.array([45,46,41,42,37,51,52,70,53,47,60,66,76,65,58,59])-1) #lparietal c4=list(np.array([87,86,93,85,92,98,103,102,97,91,84,83,90,96,101,108])-1)#rparietal c5=list(np.array([33,26,22,34,27,23,35,28,24,19,36,29,20,30,13])-1) #lfront c6=list(np.array([9,2,122,3,123,116,4,124,117,110,118,111,104,112,105])-1) #rfront cluster3={} cluster3['c1']=c1 cluster3['c2']=c2 cluster3['c3']=c3 cluster3['c4']=c4 cluster3['c5']=c5 cluster3['c6']=c6 return cluster3
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61.5 KB

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 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 DeepEye. If you want to run other methods please choose another model in the config.py file. INFO:root:(36223, 129, 500) INFO:root:********** INFO:root:--- Runtime: 11104.666312932968 seconds --- INFO:root:Finished Logging
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 epoch;accuracy;loss;val_accuracy;val_loss 0;0.5230864882469177;0.692396342754364;0.5334713459014893;0.6932186484336853 1;0.5498999357223511;0.6862275004386902;0.5228433609008789;0.7716530561447144 2;0.5852715969085693;0.6707798838615417;0.5580400228500366;0.7413833141326904 3;0.660086989402771;0.6142764687538147;0.6236025094985962;0.8171049952507019 4;0.7408033609390259;0.5223807692527771;0.7293305993080139;0.6326043605804443 5;0.7779349684715271;0.47168469429016113;0.7773637175559998;0.49084481596946716 6;0.7934985160827637;0.4436928331851959;0.7360938787460327;0.5417788028717041 7;0.8084753751754761;0.42219018936157227;0.7824706435203552;0.49352583289146423 8;0.8136172294616699;0.41002577543258667;0.7463077902793884;0.6125450134277344 9;0.8211056590080261;0.39536038041114807;0.7648032903671265;0.5579732060432434 10;0.8277313709259033;0.3848140835762024;0.7694962024688721;0.4879244267940521 11;0.8347712159156799;0.3726275861263275;0.7881297469139099;0.46677184104919434 12;0.839084804058075;0.36251911520957947;0.7917184233665466;0.45139333605766296 13;0.8429843187332153;0.3543992042541504;0.7925465703010559;0.4391316771507263 14;0.850817859172821;0.3394559323787689;0.7740510702133179;0.492371529340744 15;0.8566498756408691;0.3298674523830414;0.782608687877655;0.47639545798301697 16;0.8612740635871887;0.317749559879303;0.7725327610969543;0.519951343536377 17;0.8653806447982788;0.31075283885002136;0.8169772028923035;0.4001269042491913 18;0.8714541792869568;0.29660725593566895;0.806625247001648;0.43497729301452637 19;0.8781144022941589;0.28845372796058655;0.8111801147460938;0.45003801584243774 20;0.8821174502372742;0.2811090350151062;0.8006901144981384;0.437954843044281 21;0.8863620758056641;0.274104505777359;0.8240165710449219;0.3990015983581543 22;0.888122022151947;0.2613738775253296;0.833816409111023;0.39800161123275757 23;0.8955069184303284;0.25293490290641785;0.8414078950881958;0.3751762807369232 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60;0.9539305567741394;0.1143205463886261;0.8498274683952332;0.4431149661540985 61;0.9571744203567505;0.10902411490678787;0.8518978357315063;0.40638890862464905 62;0.9580716490745544;0.10844714194536209;0.8492753505706787;0.3995724618434906 63;0.9572089314460754;0.10668333619832993;0.8426501154899597;0.4169170558452606 64;0.9579681158065796;0.10640285909175873;0.8449965715408325;0.43724074959754944 65;0.9595209956169128;0.10299146175384521;0.8607315421104431;0.35531896352767944 66;0.959417462348938;0.10273678600788116;0.852173924446106;0.37986719608306885 67;0.9609013795852661;0.10030291229486465;0.8484472036361694;0.433697372674942 68;0.9608668684959412;0.10008957982063293;0.8545203804969788;0.4087229371070862 69;0.9616950750350952;0.10061786323785782;0.8593512773513794;0.39342328906059265 70;0.9630754590034485;0.0955834835767746;0.8483091592788696;0.4319176971912384 71;0.9620056748390198;0.09838389605283737;0.834782600402832;0.4909389019012451 72;0.9642832279205322;0.09331662952899933;0.8489993214607239;0.44009459018707275 73;0.962730348110199;0.09265079349279404;0.8582470417022705;0.390961229801178 74;0.9636965990066528;0.09317425638437271;0.8433402180671692;0.46156325936317444 75;0.9621092081069946;0.09564506262540817;0.8603174686431885;0.4048258066177368 76;0.9654910564422607;0.0874016284942627;0.8559006452560425;0.4128369092941284 77;0.9651114344596863;0.088468998670578;0.850931704044342;0.444007009267807 78;0.9664918184280396;0.08685588091611862;0.8594893217086792;0.45234087109565735 79;0.9672855138778687;0.08492127060890198;0.8263630270957947;0.530102550983429 80;0.9672510027885437;0.08538906276226044;0.8554865717887878;0.47318941354751587 81;0.9672855138778687;0.08361921459436417;0.849965512752533;0.4938923120498657 82;0.9674925804138184;0.0841403678059578;0.8590752482414246;0.4950912594795227 83;0.9680101871490479;0.08073784410953522;0.8593512773513794;0.41688409447669983 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 best_model_train_loss,best_model_val_loss,best_model_train_acc,best_model_val_acc 0.1336251199245453,0.35358092188835144,0.9472358226776123,0.8679088950157166
 loss,accuracy,val_loss,val_accuracy 0.692396342754364,0.5230864882469177,0.6932186484336853,0.5334713459014893 0.6862275004386902,0.5498999357223511,0.7716530561447144,0.5228433609008789 0.6707798838615417,0.5852715969085693,0.7413833141326904,0.5580400228500366 0.6142764687538147,0.660086989402771,0.8171049952507019,0.6236025094985962 0.5223807692527771,0.7408033609390259,0.6326043605804443,0.7293305993080139 0.47168469429016113,0.7779349684715271,0.49084481596946716,0.7773637175559998 0.4436928331851959,0.7934985160827637,0.5417788028717041,0.7360938787460327 0.42219018936157227,0.8084753751754761,0.49352583289146423,0.7824706435203552 0.41002577543258667,0.8136172294616699,0.6125450134277344,0.7463077902793884 0.39536038041114807,0.8211056590080261,0.5579732060432434,0.7648032903671265 0.3848140835762024,0.8277313709259033,0.4879244267940521,0.7694962024688721 0.3726275861263275,0.8347712159156799,0.46677184104919434,0.7881297469139099 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0.16457577049732208,0.9341569542884827,0.3796006143093109,0.8467909097671509 0.16225208342075348,0.9340534210205078,0.3695744574069977,0.8528640270233154 0.15806078910827637,0.9346055388450623,0.3670382797718048,0.8487232327461243 0.15511035919189453,0.9366760849952698,0.35177794098854065,0.8625258803367615 0.15542584657669067,0.9368831515312195,0.34946268796920776,0.8567287921905518 0.14643138647079468,0.9402305483818054,0.40815407037734985,0.8374050855636597 0.14526617527008057,0.9408516883850098,0.40169239044189453,0.8488612771034241 0.14436493813991547,0.9413347840309143,0.35436004400253296,0.8633540272712708 0.14072160422801971,0.9435088634490967,0.47024378180503845,0.8351966738700867 0.13651596009731293,0.9451997876167297,0.3763759136199951,0.8550724387168884 0.13323935866355896,0.9476499557495117,0.3562726378440857,0.8672187924385071 0.1336251199245453,0.9472358226776123,0.35358092188835144,0.8679088950157166 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 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 deepeye-lstm. If you want to run other methods please choose another model in the config.py file. INFO:root:********** INFO:root:--- Runtime: 9515.300458192825 seconds --- INFO:root:Finished Logging
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 epoch;accuracy;loss;val_accuracy;val_loss 0;0.5941404104232788;0.6602746844291687;0.7151138782501221;0.5683244466781616 1;0.8356339335441589;0.3651406466960907;0.8989648222923279;0.24608594179153442 2;0.9048243761062622;0.22609087824821472;0.6942719221115112;0.5774282813072205 3;0.9238387942314148;0.18303588032722473;0.9166321754455566;0.20516696572303772 4;0.9378839135169983;0.15185639262199402;0.9196687340736389;0.20734354853630066 5;0.9470287561416626;0.13127775490283966;0.9068322777748108;0.23466713726520538 6;0.9552419185638428;0.11420131474733353;0.9013112783432007;0.229664146900177 7;0.961833119392395;0.0982707217335701;0.9042097926139832;0.24470926821231842 8;0.9650769829750061;0.0889105275273323;0.91856449842453;0.24096529185771942 9;0.969494104385376;0.0800345242023468;0.9167701601982117;0.24692057073116302 10;0.9736006855964661;0.071156807243824;0.9221532344818115;0.19877946376800537 11;0.9768790006637573;0.06101837754249573;0.7664596438407898;0.5622040033340454 12;0.9786734580993652;0.05611290782690048;0.9134575724601746;0.29696208238601685 13;0.9799503087997437;0.053156010806560516;0.8926156163215637;0.32439571619033813 14;0.9833321571350098;0.04527267813682556;0.6909592747688293;0.970374584197998 15;0.9828490614891052;0.0477653332054615;0.91856449842453;0.30168378353118896 16;0.9850921630859375;0.04184339568018913;0.891373336315155;0.2724725008010864 17;0.9876458048820496;0.035080768167972565;0.7020013928413391;0.9725397229194641 18;0.9870936870574951;0.036235641688108444;0.9229813814163208;0.23607663810253143 19;0.9888536334037781;0.03270920738577843;0.9135955572128296;0.24799206852912903 20;0.9895437955856323;0.0295732282102108;0.9177363514900208;0.3294348120689392 21;0.9887501001358032;0.03396262228488922;0.9204968810081482;0.3118152618408203 22;0.9900959134101868;0.028041010722517967;0.9253278374671936;0.25107434391975403 23;0.9893712401390076;0.030092425644397736;0.8049689531326294;0.490396112203598 24;0.9905100464820862;0.027537183836102486;0.9158040285110474;0.2710525095462799 25;0.9919593930244446;0.02486882545053959;0.9180124402046204;0.34158164262771606 26;0.9931327104568481;0.020225519314408302;0.9118012189865112;0.36886054277420044 27;0.9909586310386658;0.025304891169071198;0.8967564105987549;0.3348923623561859 28;0.9919248819351196;0.022410022094845772;0.9145617485046387;0.3094131648540497 29;0.992408037185669;0.021294640377163887;0.9200828075408936;0.2791995108127594 30;0.9928566217422485;0.021664125844836235;0.8977225422859192;0.30948421359062195 31;0.992339015007019;0.021960925310850143;0.9134575724601746;0.2723333239555359 32;0.993201732635498;0.020935071632266045;0.9182884693145752;0.33849841356277466 33;0.9939954280853271;0.017728598788380623;0.9207729697227478;0.3764880299568176 34;0.9928221702575684;0.021129891276359558;0.9218771457672119;0.2949715852737427 35;0.9928566217422485;0.022181719541549683;0.9155279397964478;0.27820950746536255 36;0.9946166276931763;0.018577303737401962;0.9195306897163391;0.34436023235321045 37;0.9959279298782349;0.012416638433933258;0.916218101978302;0.27865859866142273 38;0.9943060278892517;0.018639812245965004;0.9240855574607849;0.3459779918193817 39;0.9937193989753723;0.01799260638654232;0.7026914954185486;1.0847797393798828 40;0.9933742880821228;0.019633324816823006;0.914147675037384;0.2913025915622711 41;0.995858907699585;0.012120447121560574;0.8979986310005188;0.347978413105011 42;0.9945821166038513;0.017072759568691254;0.7559696435928345;1.0951247215270996 43;0.9952377676963806;0.013431084342300892;0.9240855574607849;0.31902241706848145 44;0.9962385296821594;0.011204857379198074;0.9199447631835938;0.36470985412597656 45;0.9945821166038513;0.015451207756996155;0.893443763256073;0.3701016306877136 46;0.9955483675003052;0.01148987002670765;0.9217391014099121;0.3239818513393402 47;0.9936503767967224;0.017195845022797585;0.8574188947677612;0.5609152913093567 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