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Commit 907806f7 authored by Ard Kastrati's avatar Ard Kastrati
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Merge branch 'master' of https://gitlab.ethz.ch/kard/dl-project

parents 398c7dc9 1ffa2794
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cluster2.py 0 → 100644
View file @ 907806f7
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
View file @ 907806f7
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
results/best_deepeye (no architecture, remain to be uploaded)/batches.log 0 → 100644
View file @ 907806f7
epoch;accuracy;loss;val_accuracy;val_loss
0;0.6013182401657104;0.6647417545318604;0.6582470536231995;0.7188303470611572
1;0.7604044675827026;0.49373576045036316;0.7639751434326172;0.5122150778770447
2;0.8127545118331909;0.40877968072891235;0.8095238208770752;0.4329862594604492
3;0.8424667119979858;0.3561171293258667;0.8086956739425659;0.4179646074771881
4;0.8609634637832642;0.3185793459415436;0.8121463060379028;0.4069715142250061
5;0.8750431537628174;0.29031482338905334;0.6861283779144287;0.7224557399749756
6;0.8877078890800476;0.2658785879611969;0.6630780100822449;0.6044532060623169
7;0.8981986045837402;0.24282807111740112;0.8492753505706787;0.343013197183609
8;0.9054800271987915;0.22613760828971863;0.7298826575279236;0.5510682463645935
9;0.9113120436668396;0.21179983019828796;0.7769496440887451;0.4982706308364868
10;0.918627917766571;0.1961323618888855;0.7831608057022095;0.44765228033065796
11;0.9253571629524231;0.18601356446743011;0.864044189453125;0.320788711309433
12;0.930464506149292;0.172199547290802;0.6057971119880676;1.554414987564087
13;0.9357098340988159;0.16009213030338287;0.7929606437683105;0.541066586971283
14;0.9401615262031555;0.150617316365242;0.7976535558700562;0.4499603807926178
15;0.9429912567138672;0.1408129632472992;0.8658385276794434;0.3125569224357605
16;0.9470632672309875;0.13270783424377441;0.7944789528846741;0.6356551647186279
17;0.951618492603302;0.12553194165229797;0.8465148210525513;0.3625510632991791
18;0.9526192545890808;0.12019973248243332;0.8176673650741577;0.5727553963661194
19;0.9549658298492432;0.11458298563957214;0.8019323945045471;0.5455320477485657
20;0.9600041508674622;0.10186079144477844;0.8645962476730347;0.3756420314311981
21;0.9640071988105774;0.09719923138618469;0.7958592176437378;0.7014791369438171
22;0.9614534974098206;0.09791050851345062;0.5365079641342163;1.8824732303619385
23;0.9653530120849609;0.09221097081899643;0.8734299540519714;0.3538748323917389
24;0.9687694311141968;0.08267182856798172;0.883781909942627;0.3705730438232422
25;0.9701497554779053;0.07801695168018341;0.5538992285728455;1.5600996017456055
26;0.9674580693244934;0.08365857601165771;0.8734299540519714;0.4140477180480957
27;0.9707364439964294;0.07824554294347763;0.8650103807449341;0.3335997462272644
28;0.9726344347000122;0.07314753532409668;0.5474120378494263;4.001978397369385
29;0.9739112257957458;0.06988772749900818;0.8645962476730347;0.3948058485984802
30;0.9758092164993286;0.06439933180809021;0.8726018071174622;0.3486180305480957
31;0.9756022095680237;0.0669761523604393;0.7169082164764404;0.8680422306060791
32;0.977120578289032;0.06432097405195236;0.8407177329063416;0.4601145386695862
33;0.981296181678772;0.049314502626657486;0.6981366276741028;0.7999607920646667
34;0.9801228642463684;0.0538608692586422;0.8502415418624878;0.48776260018348694
35;0.9801228642463684;0.05343281477689743;0.8169772028923035;0.5628825426101685
36;0.9810200929641724;0.05339588597416878;0.8357487916946411;0.6363515257835388
37;0.9833321571350098;0.04766633361577988;0.8734299540519714;0.4085341691970825
38;0.9822624325752258;0.05083421990275383;0.8753623366355896;0.44103166460990906
39;0.9827110171318054;0.045795928686857224;0.8683229684829712;0.40721583366394043
40;0.98302161693573;0.047874074429273605;0.8830918073654175;0.38717201352119446
41;0.9839533567428589;0.0444566011428833;0.8764665126800537;0.3786880671977997
42;0.9861618876457214;0.03839310631155968;0.6445824503898621;1.440016269683838
43;0.9867485761642456;0.03467462584376335;0.754451334476471;0.9329652190208435
44;0.9860239028930664;0.039042878895998;0.8113181591033936;0.9142934083938599
45;0.9908896684646606;0.02680203877389431;0.5381642580032349;2.8352065086364746
46;0.9936158657073975;0.02031075395643711;0.8742581009864807;0.42920997738838196
47;0.9951342344284058;0.01614142209291458;0.8898550868034363;0.4254204332828522
48;0.994961678981781;0.015388195402920246;0.7980676293373108;0.8661810159683228
49;0.9953413009643555;0.014780772849917412;0.8826777338981628;0.42589181661605835
50;0.9950997233390808;0.015207239426672459;0.8864043951034546;0.4520763158798218
51;0.9957553744316101;0.013148890808224678;0.8804692625999451;0.4241575598716736
52;0.9953413009643555;0.014253922738134861;0.8764665126800537;0.4503651261329651
53;0.9957208633422852;0.01439791638404131;0.6346445679664612;1.6075189113616943
54;0.9950652122497559;0.01415100134909153;0.8628019094467163;0.5555873513221741
55;0.9948581457138062;0.015042128972709179;0.5726708173751831;1.759964942932129
56;0.9954793453216553;0.01345085259526968;0.8714975714683533;0.505007803440094
57;0.9957898855209351;0.013103947974741459;0.8808833956718445;0.45496246218681335
58;0.9959969520568848;0.011644131503999233;0.5344375371932983;4.441368579864502
59;0.9970322251319885;0.010258570313453674;0.879365086555481;0.46842095255851746
60;0.9968596696853638;0.009846488013863564;0.6578329801559448;1.9142792224884033
61;0.9965836405754089;0.011015750467777252;0.8746721744537354;0.5191388726234436
62;0.9961004853248596;0.01198595017194748;0.7118012309074402;0.6579892635345459
63;0.9964110851287842;0.011371866799890995;0.853140115737915;0.5022312998771667
64;0.9967561364173889;0.01109679602086544;0.8822636008262634;0.5340335965156555
65;0.9964110851287842;0.0106416717171669;0.8835058808326721;0.5271812677383423
66;0.9963420629501343;0.011031276546418667;0.6484472155570984;2.3088111877441406
67;0.9962040185928345;0.01210701558738947;0.8846100568771362;0.49404028058052063
68;0.9971012473106384;0.008027706295251846;0.7202208638191223;1.227425217628479
69;0.9975498914718628;0.007318778894841671;0.8172532916069031;0.7359716296195984
70;0.998240053653717;0.006512874271720648;0.8332642912864685;0.8019935488700867
71;0.9975153803825378;0.0077844285406172276;0.8905451893806458;0.48695433139801025
72;0.9978259205818176;0.006206728518009186;0.8796411156654358;0.5435582995414734
73;0.9978259205818176;0.0069560459814965725;0.8266390562057495;0.550464391708374
74;0.9975844025611877;0.007388035301119089;0.8824016451835632;0.5105237364768982
75;0.9983435869216919;0.005616242066025734;0.8859903216362;0.49452242255210876
76;0.9980674982070923;0.006255215033888817;0.8766045570373535;0.5102707743644714
77;0.9979294538497925;0.006363917142152786;0.5254658460617065;8.555727005004883
78;0.9980674982070923;0.0060712662525475025;0.8848861455917358;0.49233540892601013
79;0.9978949427604675;0.006212325766682625;0.5828847289085388;1.855910062789917
80;0.9978949427604675;0.0065247779712080956;0.8865424394607544;0.462265282869339
81;0.9983780980110168;0.004769725725054741;0.8905451893806458;0.4729859530925751
82;0.9980329871177673;0.00543142668902874;0.8418219685554504;0.7137159109115601
83;0.9979639649391174;0.006339591927826405;0.8063492178916931;0.85755854845047
84;0.9980674982070923;0.005919098854064941;0.8746721744537354;0.47671258449554443
85;0.9979984760284424;0.006197285372763872;0.884748101234436;0.4942890405654907
86;0.9983435869216919;0.006231770385056734;0.886956512928009;0.5223009586334229
87;0.9983435869216919;0.005088430363684893;0.8829537630081177;0.5294210910797119
88;0.9981020092964172;0.005834919400513172;0.6473429799079895;1.0382744073867798
89;0.9984126091003418;0.004756706766784191;0.8407177329063416;0.5431330800056458
90;0.9983435869216919;0.005154688376933336;0.8608695864677429;0.5227078795433044
91;0.9982055425643921;0.006373307667672634;0.8853002190589905;0.49868902564048767
92;0.9980329871177673;0.005687379743903875;0.5420289635658264;2.6844680309295654
93;0.9984126091003418;0.004803156945854425;0.8859903216362;0.5000370144844055
94;0.9988266825675964;0.004373767413198948;0.8824016451835632;0.5592061281204224
95;0.999137282371521;0.0032131399493664503;0.8872326016426086;0.5488330721855164
96;0.9983435869216919;0.004668352659791708;0.8685990571975708;0.540473461151123
97;0.9990682601928711;0.003421215107664466;0.5333333611488342;6.153782367706299
98;0.9988611936569214;0.0035844463855028152;0.5867494940757751;3.8600969314575195
99;0.9989992380142212;0.003647474106401205;0.878122866153717;0.5326786637306213
results/best_deepeye (no architecture, remain to be uploaded)/deepeye_df_best_model.csv 0 → 100644
View file @ 907806f7
best_model_train_loss,best_model_val_loss,best_model_train_acc,best_model_val_acc
0.0077844285406172276,0.48695433139801025,0.9975153803825378,0.8905451893806458
results/best_deepeye (no architecture, remain to be uploaded)/deepeye_history.csv 0 → 100644
View file @ 907806f7
loss,accuracy,val_loss,val_accuracy,lr
0.6647417545318604,0.6013182401657104,0.7188303470611572,0.6582470536231995,0.0010000000474974513
0.49373576045036316,0.7604044675827026,0.5122150778770447,0.7639751434326172,0.0010000000474974513
0.40877968072891235,0.8127545118331909,0.4329862594604492,0.8095238208770752,0.0010000000474974513
0.3561171293258667,0.8424667119979858,0.4179646074771881,0.8086956739425659,0.0010000000474974513
0.3185793459415436,0.8609634637832642,0.4069715142250061,0.8121463060379028,0.0010000000474974513
0.29031482338905334,0.8750431537628174,0.7224557399749756,0.6861283779144287,0.0010000000474974513
0.2658785879611969,0.8877078890800476,0.6044532060623169,0.6630780100822449,0.0010000000474974513
0.24282807111740112,0.8981986045837402,0.343013197183609,0.8492753505706787,0.0010000000474974513
0.22613760828971863,0.9054800271987915,0.5510682463645935,0.7298826575279236,0.0010000000474974513
0.21179983019828796,0.9113120436668396,0.4982706308364868,0.7769496440887451,0.0010000000474974513
0.1961323618888855,0.918627917766571,0.44765228033065796,0.7831608057022095,0.0010000000474974513
0.18601356446743011,0.9253571629524231,0.320788711309433,0.864044189453125,0.0010000000474974513
0.172199547290802,0.930464506149292,1.554414987564087,0.6057971119880676,0.0010000000474974513
0.16009213030338287,0.9357098340988159,0.541066586971283,0.7929606437683105,0.0010000000474974513
0.150617316365242,0.9401615262031555,0.4499603807926178,0.7976535558700562,0.0010000000474974513
0.1408129632472992,0.9429912567138672,0.3125569224357605,0.8658385276794434,0.0010000000474974513
0.13270783424377441,0.9470632672309875,0.6356551647186279,0.7944789528846741,0.0010000000474974513
0.12553194165229797,0.951618492603302,0.3625510632991791,0.8465148210525513,0.0010000000474974513
0.12019973248243332,0.9526192545890808,0.5727553963661194,0.8176673650741577,0.0010000000474974513
0.11458298563957214,0.9549658298492432,0.5455320477485657,0.8019323945045471,0.0010000000474974513
0.10186079144477844,0.9600041508674622,0.3756420314311981,0.8645962476730347,0.0010000000474974513
0.09719923138618469,0.9640071988105774,0.7014791369438171,0.7958592176437378,0.0010000000474974513
0.09791050851345062,0.9614534974098206,1.8824732303619385,0.5365079641342163,0.0010000000474974513
0.09221097081899643,0.9653530120849609,0.3538748323917389,0.8734299540519714,0.0010000000474974513
0.08267182856798172,0.9687694311141968,0.3705730438232422,0.883781909942627,0.0010000000474974513
0.07801695168018341,0.9701497554779053,1.5600996017456055,0.5538992285728455,0.0010000000474974513
0.08365857601165771,0.9674580693244934,0.4140477180480957,0.8734299540519714,0.0010000000474974513
0.07824554294347763,0.9707364439964294,0.3335997462272644,0.8650103807449341,0.0010000000474974513
0.07314753532409668,0.9726344347000122,4.001978397369385,0.5474120378494263,0.0010000000474974513
0.06988772749900818,0.9739112257957458,0.3948058485984802,0.8645962476730347,0.0010000000474974513
0.06439933180809021,0.9758092164993286,0.3486180305480957,0.8726018071174622,0.0010000000474974513
0.0669761523604393,0.9756022095680237,0.8680422306060791,0.7169082164764404,0.0010000000474974513
0.06432097405195236,0.977120578289032,0.4601145386695862,0.8407177329063416,0.0010000000474974513
0.049314502626657486,0.981296181678772,0.7999607920646667,0.6981366276741028,0.0010000000474974513
0.0538608692586422,0.9801228642463684,0.48776260018348694,0.8502415418624878,0.0010000000474974513
0.05343281477689743,0.9801228642463684,0.5628825426101685,0.8169772028923035,0.0010000000474974513
0.05339588597416878,0.9810200929641724,0.6363515257835388,0.8357487916946411,0.0010000000474974513
0.04766633361577988,0.9833321571350098,0.4085341691970825,0.8734299540519714,0.0010000000474974513
0.05083421990275383,0.9822624325752258,0.44103166460990906,0.8753623366355896,0.0010000000474974513
0.045795928686857224,0.9827110171318054,0.40721583366394043,0.8683229684829712,0.0010000000474974513
0.047874074429273605,0.98302161693573,0.38717201352119446,0.8830918073654175,0.0010000000474974513
0.0444566011428833,0.9839533567428589,0.3786880671977997,0.8764665126800537,0.0010000000474974513
0.03839310631155968,0.9861618876457214,1.440016269683838,0.6445824503898621,0.0010000000474974513
0.03467462584376335,0.9867485761642456,0.9329652190208435,0.754451334476471,0.0010000000474974513
0.039042878895998,0.9860239028930664,0.9142934083938599,0.8113181591033936,0.0010000000474974513
0.02680203877389431,0.9908896684646606,2.8352065086364746,0.5381642580032349,0.0005000000237487257
0.02031075395643711,0.9936158657073975,0.42920997738838196,0.8742581009864807,0.0005000000237487257
0.01614142209291458,0.9951342344284058,0.4254204332828522,0.8898550868034363,0.0005000000237487257
0.015388195402920246,0.994961678981781,0.8661810159683228,0.7980676293373108,0.0005000000237487257
0.014780772849917412,0.9953413009643555,0.42589181661605835,0.8826777338981628,0.0005000000237487257
0.015207239426672459,0.9950997233390808,0.4520763158798218,0.8864043951034546,0.0005000000237487257
0.013148890808224678,0.9957553744316101,0.4241575598716736,0.8804692625999451,0.0005000000237487257
0.014253922738134861,0.9953413009643555,0.4503651261329651,0.8764665126800537,0.0005000000237487257
0.01439791638404131,0.9957208633422852,1.6075189113616943,0.6346445679664612,0.0005000000237487257
0.01415100134909153,0.9950652122497559,0.5555873513221741,0.8628019094467163,0.0005000000237487257
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results/best_deepeye (no architecture, remain to be uploaded)/info.log 0 → 100644
View file @ 907806f7
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
results/best_deepeye-lstm/batches.log 0 → 100644
View file @ 907806f7
epoch;accuracy;loss;val_accuracy;val_loss
0;0.5230864882469177;0.692396342754364;0.5334713459014893;0.6932186484336853
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results/best_deepeye-lstm/deepeye-lstm_df_best_model.csv 0 → 100644
View file @ 907806f7
best_model_train_loss,best_model_val_loss,best_model_train_acc,best_model_val_acc
0.1336251199245453,0.35358092188835144,0.9472358226776123,0.8679088950157166
results/best_deepeye-lstm/deepeye-lstm_history.csv 0 → 100644
View file @ 907806f7
loss,accuracy,val_loss,val_accuracy
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results/best_deepeye-lstm/info.log 0 → 100644
View file @ 907806f7
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
results/best_deepeye-lstm/lsf.o13439614 0 → 100644
View file @ 907806f7
This diff is collapsed.
results/best_deepeye3/batches.log 0 → 100644
View file @ 907806f7
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
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3;0.9238387942314148;0.18303588032722473;0.9166321754455566;0.20516696572303772
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68;0.996721625328064;0.00982312299311161;0.9200828075408936;0.38851556181907654
69;0.9974808692932129;0.007991242222487926;0.927122175693512;0.34160953760147095
70;0.9976188540458679;0.007950046099722385;0.8600413799285889;0.636195957660675
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