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Commit 0669c717 authored by Ard Kastrati's avatar Ard Kastrati
Browse files

Resolved conflicts

parents 4c7493fe 0cf3c8a4
......@@ -149,7 +149,7 @@ class Classifier_EEGNet:
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(eegnet_x, y, test_size=0.2, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(eegnet_x, y, test_size=0.199182, shuffle=False)
pred_ensemble = prediction_history((X_val,y_val))
hist = self.model.fit(X_train, y_train, verbose=1, validation_data=(X_val,y_val),
epochs=self.epochs, callbacks=[csv_logger, ckpt, early_stop,pred_ensemble])
......
......@@ -42,7 +42,7 @@ Cluster can be set to clustering(), clustering2() or clustering3(), where differ
"""
# Choosing model
config['model'] = 'pyramidal_cnn'
config['model'] = 'eegnet'
config['downsampled'] = False
config['split'] = False
config['cluster'] = clustering()
......
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29;0.8839974999427795;0.2731625735759735;0.8503118753433228;0.35925430059432983
30;0.8837906718254089;0.2694026529788971;0.8715176582336426;0.33729586005210876
31;0.8865485191345215;0.2639593482017517;0.8561330437660217;0.3992648720741272
32;0.8892374634742737;0.26308751106262207;0.8698544502258301;0.3187195360660553
33;0.8866519331932068;0.26519426703453064;0.844490647315979;0.37541067600250244
34;0.8890995383262634;0.26319029927253723;0.8705474734306335;0.3239564597606659
35;0.8876861333847046;0.2630176544189453;0.863617479801178;0.3382852077484131
36;0.8894787430763245;0.25979599356651306;0.8663894534111023;0.3399188220500946
37;0.8903750777244568;0.2571861743927002;0.7771309614181519;0.46765631437301636
38;0.8894787430763245;0.2570141851902008;0.7898821830749512;0.46086350083351135
39;0.8918918967247009;0.2521525025367737;0.8625086545944214;0.3491903245449066
40;0.8940637111663818;0.25003957748413086;0.865557849407196;0.3404541313648224
41;0.8954771161079407;0.24790148437023163;0.8693000674247742;0.3365233242511749
42;0.894511878490448;0.2502857744693756;0.8708246946334839;0.3312726616859436
43;0.8951668739318848;0.24869941174983978;0.8693000674247742;0.340474396944046
44;0.8956494927406311;0.24336747825145721;0.8598752617835999;0.36092284321784973
45;0.8968560099601746;0.2466135025024414;0.8704088926315308;0.32102301716804504
46;0.8997862935066223;0.2411406934261322;0.8634788393974304;0.3417678475379944
47;0.897442102432251;0.2464517503976822;0.8801108598709106;0.31466177105903625
48;0.8977178931236267;0.24229022860527039;0.8546084761619568;0.45236632227897644
49;0.8995449542999268;0.2381962388753891;0.8605682849884033;0.3710474371910095
0;0.7307639122009277;0.5430943965911865;0.7477477192878723;0.5700396299362183
1;0.7818877696990967;0.4727802276611328;0.7922384142875671;0.48174384236335754
2;0.7962630987167358;0.44739463925361633;0.800970196723938;0.4553138315677643
3;0.8038127422332764;0.42907610535621643;0.7233541011810303;0.6052377820014954
4;0.8151544332504272;0.40801602602005005;0.8424116373062134;0.3930226266384125
5;0.8314947485923767;0.3804503083229065;0.8352044224739075;0.5178271532058716
6;0.8408370018005371;0.36000362038612366;0.8270270228385925;0.4246981739997864
7;0.8491106033325195;0.3479642868041992;0.853638231754303;0.357673317193985
8;0.8530750274658203;0.33983471989631653;0.8501732349395752;0.36370915174484253
9;0.8567636609077454;0.33289629220962524;0.8558558821678162;0.3549887239933014
10;0.8573152422904968;0.3289293944835663;0.8576576709747314;0.35147425532341003
11;0.8611072897911072;0.32244279980659485;0.6302148103713989;0.5768260955810547
12;0.8642787933349609;0.31551143527030945;0.8148302435874939;0.4258965253829956
13;0.8664506077766418;0.3123764097690582;0.8533610701560974;0.3777877390384674
14;0.8645201325416565;0.31223928928375244;0.8589050769805908;0.34425124526023865
15;0.8697945475578308;0.30345389246940613;0.8547470569610596;0.36819469928741455
16;0.8716561198234558;0.29989174008369446;0.8638946413993835;0.34392398595809937
17;0.8699669241905212;0.30042585730552673;0.8601524829864502;0.3421225845813751
18;0.8713803291320801;0.2975643277168274;0.8225918412208557;0.44518810510635376
19;0.8738279342651367;0.29557567834854126;0.8670824766159058;0.33399340510368347
20;0.8744484186172485;0.2913721799850464;0.6299376487731934;0.5719832181930542
21;0.8772752285003662;0.2886054217815399;0.8611226677894592;0.34384942054748535
22;0.8770339488983154;0.2883927822113037;0.8467082381248474;0.41120100021362305
23;0.8804467916488647;0.2816269099712372;0.852113664150238;0.36345773935317993
24;0.8789299726486206;0.28175145387649536;0.8595980405807495;0.33612266182899475
25;0.878998875617981;0.27983441948890686;0.860291063785553;0.34895041584968567
26;0.8805156946182251;0.27922213077545166;0.8304920196533203;0.3941403925418854
27;0.8824806809425354;0.27465906739234924;0.8715176582336426;0.32740429043769836
28;0.8829288482666016;0.27380645275115967;0.8537768721580505;0.34692564606666565
29;0.8855832815170288;0.2704719603061676;0.866943895816803;0.3300798237323761
30;0.8866519331932068;0.26738685369491577;0.8575190305709839;0.3552154004573822
31;0.8879274725914001;0.26719287037849426;0.8706860542297363;0.3248162269592285
32;0.8871346116065979;0.26497822999954224;0.8690228462219238;0.32825446128845215
33;0.8884790539741516;0.25943076610565186;0.8591822385787964;0.39723536372184753
34;0.8925123810768127;0.25619980692863464;0.8469854593276978;0.4583474397659302
35;0.8913058638572693;0.25575804710388184;0.8467082381248474;0.3786676228046417
36;0.891167938709259;0.2574975788593292;0.8747054934501648;0.32031166553497314
37;0.891960859298706;0.2544667422771454;0.8659736514091492;0.3410598635673523
38;0.8929950594902039;0.24929837882518768;0.8680526614189148;0.3334028720855713
39;0.8947876691818237;0.25071677565574646;0.872210681438446;0.3203766942024231
40;0.8924089670181274;0.2539658546447754;0.8661122918128967;0.32616227865219116
41;0.8955460786819458;0.2460424304008484;0.8666666746139526;0.3329457640647888
42;0.8968215584754944;0.24464955925941467;0.8558558821678162;0.44708776473999023
43;0.8967525959014893;0.2433656007051468;0.8733194470405579;0.3270375430583954
44;0.8955115675926208;0.24719250202178955;0.8730422854423523;0.3188692331314087
45;0.8963044881820679;0.24475790560245514;0.8785862922668457;0.3201245069503784
46;0.8973041772842407;0.24164777994155884;0.8790020942687988;0.3219301700592041
47;0.8993381261825562;0.2400384247303009;0.8717948794364929;0.32688772678375244
48;0.9011996984481812;0.23675328493118286;0.790020763874054;0.45659369230270386
49;0.9020270109176636;0.23573149740695953;0.7391545176506042;0.49796995520591736
best_model_train_loss,best_model_val_loss,best_model_train_acc,best_model_val_acc
0.2539658546447754,0.29858189821243286,0.8924089670181274,0.883021483021483
loss,accuracy,val_loss,val_accuracy
0.5430943965911865,0.7307639122009277,0.5070092082023621,0.7822591822591822
0.4727802276611328,0.7818877696990967,0.47480544447898865,0.8023562023562023
0.44739463925361633,0.7962630987167358,0.4742720127105713,0.8206514206514206
0.42907610535621643,0.8038127422332764,0.4607694745063782,0.832986832986833
0.40801602602005005,0.8151544332504272,0.3869175910949707,0.8511434511434511
0.3804503083229065,0.8314947485923767,0.39280855655670166,0.8572418572418572
0.36000362038612366,0.8408370018005371,0.39168745279312134,0.8579348579348579
0.3479642868041992,0.8491106033325195,0.37762129306793213,0.8595980595980596
0.33983471989631653,0.8530750274658203,0.3500423729419708,0.8623700623700624
0.33289629220962524,0.8567636609077454,0.3959655165672302,0.8547470547470547
0.3289293944835663,0.8573152422904968,0.3313871920108795,0.8679140679140679
0.32244279980659485,0.8611072897911072,0.3781185448169708,0.8661122661122661
0.31551143527030945,0.8642787933349609,0.34391459822654724,0.861954261954262
0.3123764097690582,0.8664506077766418,0.33309102058410645,0.8717948717948718
0.31223928928375244,0.8645201325416565,0.35016411542892456,0.8686070686070686
0.30345389246940613,0.8697945475578308,0.329543799161911,0.8708246708246709
0.29989174008369446,0.8716561198234558,0.36490821838378906,0.8587664587664587
0.30042585730552673,0.8699669241905212,0.3315175175666809,0.86999306999307
0.2975643277168274,0.8713803291320801,0.4039950966835022,0.863063063063063
0.29557567834854126,0.8738279342651367,0.3329606354236603,0.8705474705474705
0.2913721799850464,0.8744484186172485,0.3716769218444824,0.8652806652806653
0.2886054217815399,0.8772752285003662,0.3759078085422516,0.8705474705474705
0.2883927822113037,0.8770339488983154,0.3414836823940277,0.8738738738738738
0.2816269099712372,0.8804467916488647,0.31051480770111084,0.8726264726264726
0.28175145387649536,0.8789299726486206,0.31791946291923523,0.8726264726264726
0.27983441948890686,0.878998875617981,0.31719455122947693,0.8737352737352737
0.27922213077545166,0.8805156946182251,0.3421160876750946,0.8735966735966736
0.27465906739234924,0.8824806809425354,0.31257864832878113,0.8747054747054747
0.27380645275115967,0.8829288482666016,0.3226964473724365,0.8755370755370755
0.2704719603061676,0.8855832815170288,0.3115777373313904,0.87997227997228
0.26738685369491577,0.8866519331932068,0.31832119822502136,0.8762300762300762
0.26719287037849426,0.8879274725914001,0.32201823592185974,0.8751212751212751
0.26497822999954224,0.8871346116065979,0.31416741013526917,0.8794178794178794
0.25943076610565186,0.8884790539741516,0.3274582326412201,0.8744282744282744
0.25619980692863464,0.8925123810768127,0.3152606189250946,0.8796950796950797
0.25575804710388184,0.8913058638572693,0.3251776099205017,0.8727650727650728
0.2574975788593292,0.891167938709259,0.328811913728714,0.87997227997228
0.2544667422771454,0.891960859298706,0.3254774212837219,0.8748440748440749
0.24929837882518768,0.8929950594902039,0.32319846749305725,0.877061677061677
0.25071677565574646,0.8947876691818237,0.3267544209957123,0.8751212751212751
0.2539658546447754,0.8924089670181274,0.29858189821243286,0.883021483021483
0.2460424304008484,0.8955460786819458,0.3193633258342743,0.87997227997228
0.24464955925941467,0.8968215584754944,0.3392893970012665,0.8784476784476785
0.2433656007051468,0.8967525959014893,0.3069082200527191,0.8794178794178794
0.24719250202178955,0.8955115675926208,0.3194589614868164,0.8727650727650728
0.24475790560245514,0.8963044881820679,0.3469223380088806,0.8751212751212751
0.24164777994155884,0.8973041772842407,0.31420379877090454,0.8816354816354817
0.2400384247303009,0.8993381261825562,0.30562588572502136,0.8814968814968815
0.23675328493118286,0.9011996984481812,0.34437432885169983,0.879002079002079
0.23573149740695953,0.9020270109176636,0.33962270617485046,0.8772002772002772
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:(36223, 129, 500)
INFO:root:Started running eegnet. If you want to run other methods please choose another model in the config.py file.
INFO:root:Parameters...
INFO:root:--------------- chans : 129
INFO:root:--------------- samples : 500
INFO:root:--------------- dropoutRate : 0.5
INFO:root:--------------- kernLength : 64
INFO:root:--------------- F1 : 32
INFO:root:--------------- D : 8
INFO:root:--------------- F2 : 512
INFO:root:--------------- norm_rate : 0.5
WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0196s vs `on_train_batch_end` time: 0.0461s). Check your callbacks.
INFO:root:Parameters...
INFO:root:--------------- chans : 129
INFO:root:--------------- samples : 500
INFO:root:--------------- dropoutRate : 0.5
INFO:root:--------------- kernLength : 64
INFO:root:--------------- F1 : 32
INFO:root:--------------- D : 8
INFO:root:--------------- F2 : 512
INFO:root:--------------- norm_rate : 0.5
INFO:root:Parameters...
INFO:root:--------------- chans : 129
INFO:root:--------------- samples : 500
INFO:root:--------------- dropoutRate : 0.5
INFO:root:--------------- kernLength : 64
INFO:root:--------------- F1 : 32
INFO:root:--------------- D : 8
INFO:root:--------------- F2 : 512
INFO:root:--------------- norm_rate : 0.5
WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0193s vs `on_train_batch_end` time: 0.0426s). Check your callbacks.
INFO:root:Parameters...
INFO:root:--------------- chans : 129
INFO:root:--------------- samples : 500
INFO:root:--------------- dropoutRate : 0.5
INFO:root:--------------- kernLength : 64
INFO:root:--------------- F1 : 32
INFO:root:--------------- D : 8
INFO:root:--------------- F2 : 512
INFO:root:--------------- norm_rate : 0.5
WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0223s vs `on_train_batch_end` time: 0.0448s). Check your callbacks.
INFO:root:Parameters...
INFO:root:--------------- chans : 129
INFO:root:--------------- samples : 500
INFO:root:--------------- dropoutRate : 0.5
INFO:root:--------------- kernLength : 64
INFO:root:--------------- F1 : 32
INFO:root:--------------- D : 8
INFO:root:--------------- F2 : 512
INFO:root:--------------- norm_rate : 0.5
INFO:root:**********
INFO:root:--- Runtime: 15647.785918235779 seconds ---
INFO:root:Finished Logging
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