Commit bf70e31c authored by Ard Kastrati's avatar Ard Kastrati
Browse files

Added all results for pyramidal CNN

parent 74df1c7f
......@@ -24,4 +24,4 @@ class Classifier_PyramidalCNN(ConvNet):
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation(activation='relu')(x)
x = tf.keras.layers.MaxPool1D(pool_size=2, strides=2)(x)
return x
\ No newline at end of file
return x
......@@ -48,7 +48,7 @@ config['split'] = False
config['cluster'] = clustering()
config['ensemble'] = 1 #number of models in the ensemble method
config['ensemble'] = 5 #number of models in the ensemble method
config['trainX_file'] = 'noweEEG.mat' if config['downsampled'] else 'all_EEGprocuesan.mat'
config['trainY_file'] = 'all_trialinfoprosan.mat'
......
......@@ -35,9 +35,11 @@ def run(trainX, trainY):
elif config['model'] == 'cnn':
classifier = Classifier_CNN(input_shape=config['cnn']['input_shape'])
elif config['model'] == 'pyramidal_cnn':
classifier = Classifier_PyramidalCNN(input_shape=config['cnn']['input_shape'])
classifier = Classifier_PyramidalCNN(input_shape=config['cnn']['input_shape'], epochs=50)
elif config['model'] == 'eegnet':
classifier = Classifier_EEGNet()
classifier = Classifier_EEGNet(dropoutRate = 0.5, kernLength = 250, F1 = 16,
D = 4, F2 = 256, norm_rate = 0.5, dropoutType = 'Dropout',
epochs = 50)
elif config['model'] == 'inception':
classifier = Classifier_INCEPTION(input_shape=config['inception']['input_shape'], use_residual=True,
kernel_size=64, nb_filters=16, depth=12, bottleneck_size=16, epochs=50)
......
......@@ -34,4 +34,4 @@ def main():
logging.info('Finished Logging')
if __name__=='__main__':
main()
\ No newline at end of file
main()
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28;0.9943750500679016;0.01580182835459709;0.9028295278549194;0.4297197461128235
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31;0.9953413009643555;0.01352678146213293;0.9060041308403015;0.3816816806793213
32;0.9959624409675598;0.011144686490297318;0.9210489988327026;0.3691413700580597
33;0.9942370057106018;0.016404597088694572;0.8513457775115967;0.7369245886802673
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39;0.9977914094924927;0.007832501083612442;0.6652864217758179;2.2965688705444336
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43;0.996721625328064;0.00970374420285225;0.9200828075408936;0.35374221205711365
44;0.9968941807746887;0.00767890177667141;0.9196687340736389;0.4195770025253296
45;0.9946856498718262;0.015042155049741268;0.8905451893806458;0.4803517460823059
46;0.9974808692932129;0.00801975280046463;0.9227052927017212;0.3785085082054138
47;0.9983090758323669;0.005995817482471466;0.8317460417747498;0.965284526348114
48;0.9968941807746887;0.009439212270081043;0.9044858813285828;0.47222891449928284
49;0.9959969520568848;0.0120845353230834;0.9307108521461487;0.3536800742149353
0;0.7286907434463501;0.5229793190956116;0.7849551439285278;0.45761021971702576
1;0.8308026790618896;0.3748096525669098;0.8503795862197876;0.3424251973628998
2;0.8919870257377625;0.25542691349983215;0.8859903216362;0.2702254056930542
3;0.9171095490455627;0.19659088551998138;0.8659765124320984;0.29531779885292053
4;0.9324315190315247;0.16168683767318726;0.8866804838180542;0.2650527358055115
5;0.9446821808815002;0.13632336258888245;0.8535541892051697;0.332936555147171
6;0.9528262615203857;0.11832697689533234;0.9123533368110657;0.21727733314037323
7;0.9597970843315125;0.09940213710069656;0.8549344539642334;0.3707274794578552
8;0.9652494788169861;0.0888088122010231;0.9258798956871033;0.19697731733322144
9;0.9708744287490845;0.07452460378408432;0.9090406894683838;0.2638109028339386
10;0.9750845432281494;0.06493431329727173;0.8836438655853271;0.33758920431137085
11;0.9787769913673401;0.0541088841855526;0.9218771457672119;0.22271092236042023
12;0.9825729727745056;0.04514310508966446;0.9152519106864929;0.2598525285720825
13;0.9872316718101501;0.03617515787482262;0.8126984238624573;0.6470108032226562
14;0.9840568900108337;0.04141424223780632;0.9050379395484924;0.31664425134658813
15;0.9892331957817078;0.02809196524322033;0.6777087450027466;1.784716010093689
16;0.988163411617279;0.031985290348529816;0.836991012096405;0.7451400756835938
17;0.9916833639144897;0.022428086027503014;0.8179433941841125;0.7668188214302063
18;0.9899923801422119;0.026490220800042152;0.8848861455917358;0.49354374408721924
19;0.9904755353927612;0.025913532823324203;0.9025534987449646;0.33634841442108154
20;0.9935468435287476;0.019009703770279884;0.9004830718040466;0.4073377847671509
21;0.9919593930244446;0.021951014176011086;0.9094548225402832;0.39945483207702637
22;0.9925460815429688;0.02061690390110016;0.9158040285110474;0.31751471757888794
23;0.9930636882781982;0.019781801849603653;0.9169082045555115;0.3200090825557709
24;0.9937539100646973;0.017480894923210144;0.7850931882858276;0.7377864122390747
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28;0.9933397769927979;0.017822811380028725;0.9216011166572571;0.3250860273838043
29;0.9945476055145264;0.01794067770242691;0.8748102188110352;0.43100032210350037
30;0.9947891235351562;0.014074278995394707;0.9006211161613464;0.356444776058197
31;0.9939609169960022;0.018548591062426567;0.9235334992408752;0.32789942622184753
32;0.9964110851287842;0.010394802317023277;0.8989648222923279;0.39297646284103394
33;0.9948581457138062;0.014689675532281399;0.8850241303443909;0.31825318932533264
34;0.994961678981781;0.013839841820299625;0.8685990571975708;0.5844924449920654
35;0.995858907699585;0.010851029306650162;0.9066942930221558;0.5026786923408508
36;0.9961349964141846;0.012510291300714016;0.9061421751976013;0.5156367421150208
37;0.9957553744316101;0.01152802538126707;0.8647342920303345;0.6608863472938538
38;0.9982055425643921;0.005823355168104172;0.9142857193946838;0.458469033241272
39;0.9942024946212769;0.01612170971930027;0.8968943953514099;0.5346419215202332
40;0.9955828785896301;0.013920621015131474;0.8985507488250732;0.48258233070373535
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47;0.996721625328064;0.009486435912549496;0.8731539249420166;0.7880004644393921
48;0.9964455962181091;0.008878315798938274;0.9040717482566833;0.4809378683567047
49;0.996549129486084;0.009336929768323898;0.8709454536437988;0.648162841796875
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 pyramidal_cnn. If you want to run other methods please choose another model in the config.py file.
INFO:root:Parameters:
INFO:root:--------------- use residual : False
INFO:root:--------------- depth : 6
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 16
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : False
INFO:root:--------------- depth : 6
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 16
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : False
INFO:root:--------------- depth : 6
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 16
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : False
INFO:root:--------------- depth : 6
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 16
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : False
INFO:root:--------------- depth : 6
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 16
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:**********
INFO:root:--- Runtime: 3552.163224220276 seconds ---
INFO:root:Finished Logging
best_model_train_loss,best_model_val_loss,best_model_train_acc,best_model_val_acc
0.014074278995394707,0.1619231104850769,0.9947891235351562,0.9387163561076605
loss,accuracy,val_loss,val_accuracy
0.5229793190956116,0.7286907434463501,0.4002792537212372,0.824568668046929
0.3748096525669098,0.8308026790618896,0.28798213601112366,0.895376121463078
0.25542691349983215,0.8919870257377625,0.24307061731815338,0.8989648033126294
0.19659088551998138,0.9171095490455627,0.21363912522792816,0.9247757073844031
0.16168683767318726,0.9324315190315247,0.21933799982070923,0.9180124223602485
0.13632336258888245,0.9446821808815002,0.22479282319545746,0.9137336093857833
0.11832697689533234,0.9528262615203857,0.19527417421340942,0.9214630779848171
0.09940213710069656,0.9597970843315125,0.26611241698265076,0.8829537612146308
0.0888088122010231,0.9652494788169861,0.3040655851364136,0.8623878536922015
0.07452460378408432,0.9708744287490845,0.19487033784389496,0.9318150448585231
0.06493431329727173,0.9750845432281494,0.16251970827579498,0.9363699102829538
0.0541088841855526,0.9787769913673401,0.23134759068489075,0.8956521739130435
0.04514310508966446,0.9825729727745056,0.1648266315460205,0.9327812284334024
0.03617515787482262,0.9872316718101501,0.22155921161174774,0.90904071773637
0.04141424223780632,0.9840568900108337,0.17723608016967773,0.9319530710835059
0.02809196524322033,0.9892331957817078,0.23127569258213043,0.9228433402346445
0.031985290348529816,0.988163411617279,0.2066156566143036,0.9146997929606625
0.022428086027503014,0.9916833639144897,0.240288645029068,0.9101449275362319
0.026490220800042152,0.9899923801422119,0.17816978693008423,0.9330572808833678
0.025913532823324203,0.9904755353927612,0.17431998252868652,0.9359558316080056
0.019009703770279884,0.9935468435287476,0.1768779158592224,0.9315389924085576
0.021951014176011086,0.9919593930244446,0.16628137230873108,0.937888198757764
0.02061690390110016,0.9925460815429688,0.24237284064292908,0.9086266390614217
0.019781801849603653,0.9930636882781982,0.1749597191810608,0.9323671497584541
0.017480894923210144,0.9937539100646973,0.21324694156646729,0.9206349206349206
0.01854133792221546,0.9926496148109436,0.19413962960243225,0.9244996549344375
0.016374070197343826,0.9939609169960022,0.17688243091106415,0.9318150448585231
0.012890681624412537,0.9956173896789551,0.18018275499343872,0.9272601794340924
0.017822811380028725,0.9933397769927979,0.1621977984905243,0.9354037267080745
0.01794067770242691,0.9945476055145264,0.17774489521980286,0.9337474120082816
0.014074278995394707,0.9947891235351562,0.1619231104850769,0.9387163561076605
0.018548591062426567,0.9939609169960022,0.1732415109872818,0.936231884057971
0.010394802317023277,0.9964110851287842,0.18912450969219208,0.9305728088336784
0.014689675532281399,0.9948581457138062,0.18225820362567902,0.9327812284334024
0.013839841820299625,0.994961678981781,0.21405284106731415,0.916632160110421
0.010851029306650162,0.995858907699585,0.1782805174589157,0.9342995169082126
0.012510291300714016,0.9961349964141846,0.18734367191791534,0.9268461007591442
0.01152802538126707,0.9957553744316101,0.18014012277126312,0.9336093857832988
0.005823355168104172,0.9982055425643921,0.18190234899520874,0.9342995169082126
0.01612170971930027,0.9942024946212769,0.22751621901988983,0.9302967563837129
0.013920621015131474,0.9955828785896301,0.178383469581604,0.9341614906832298
0.011387034319341183,0.9962385296821594,0.18261277675628662,0.93567977915804
0.01182890497148037,0.9957208633422852,0.1837872564792633,0.9352657004830918
0.009694692678749561,0.996514618396759,0.1846180260181427,0.9300207039337474
0.007035564631223679,0.9975498914718628,0.1772298812866211,0.9325051759834369
0.008658022619783878,0.9963075518608093,0.17883582413196564,0.9342995169082126
0.01129349134862423,0.99565190076828,0.18449130654335022,0.9302967563837129
0.009486435912549496,0.996721625328064,0.18653514981269836,0.9311249137336094
0.008878315798938274,0.9964455962181091,0.20953772962093353,0.9286404416839199
0.009336929768323898,0.996549129486084,0.2040371596813202,0.9225672877846791
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