Commit c4e6164c authored by Martyna Plomecka's avatar Martyna Plomecka
Browse files

Resolved conflicts

parents 28bdd871 c8634d7a
......@@ -11,6 +11,12 @@ class Classifier_CNN(ConvNet):
The Classifier_CNN is one of the simplest classifiers. It implements the class ConvNet, which is made of modules with a specific depth.
"""
def __init__(self, input_shape, kernel_size=64, epochs = 50, nb_filters=16, verbose=True, batch_size=64, use_residual=True, depth=12):
super(Classifier_CNN, self).__init__(input_shape, kernel_size=kernel_size, epochs=epochs, nb_filters=nb_filters,
verbose=verbose, batch_size=batch_size, use_residual=use_residual,
depth=depth)
def _module(self, input_tensor, current_depth):
"""
The module of CNN is made of a simple convolution with batch normalization and ReLu activation. Finally, MaxPooling is also used.
......@@ -19,5 +25,5 @@ class Classifier_CNN(ConvNet):
x = tf.keras.layers.Conv1D(filters=self.nb_filters, kernel_size=self.kernel_size, padding='same', use_bias=False)(input_tensor)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation(activation='relu')(x)
x = tf.keras.layers.MaxPool1D()(x)
x = tf.keras.layers.MaxPool1D(pool_size=2, strides=1, padding='same')(x)
return x
......@@ -43,6 +43,7 @@ Cluster can be set to clustering(), clustering2() or clustering3(), where differ
# Choosing model
config['model'] = 'eegnet'
config['model'] = 'cnn'
config['downsampled'] = False
config['split'] = False
config['cluster'] = clustering()
......
......@@ -33,7 +33,8 @@ def run(trainX, trainY):
if config['model'] == 'deepeye':
classifier = Classifier_DEEPEYE(input_shape=config['deepeye']['input_shape'])
elif config['model'] == 'cnn':
classifier = Classifier_CNN(input_shape=config['cnn']['input_shape'])
classifier = Classifier_CNN(input_shape=config['cnn']['input_shape'], kernel_size=64, epochs = 50,
nb_filters=16, verbose=True, batch_size=64, use_residual=True, depth=12)
elif config['model'] == 'pyramidal_cnn':
classifier = Classifier_PyramidalCNN(input_shape=config['cnn']['input_shape'], epochs=50)
elif config['model'] == 'eegnet':
......
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epoch;accuracy;loss;val_accuracy;val_loss
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22;0.9633514881134033;0.09500426799058914;0.8895789980888367;0.32916662096977234
23;0.9635240435600281;0.09326154738664627;0.811042070388794;0.5016536116600037
24;0.9675961136817932;0.08348514884710312;0.8425120711326599;0.41893330216407776
25;0.9666298627853394;0.08535955101251602;0.7429951429367065;0.763941764831543
26;0.9693905711174011;0.07910985499620438;0.8734299540519714;0.3643494248390198
27;0.973773181438446;0.07030004262924194;0.718426525592804;0.8295164704322815
28;0.9745669364929199;0.06730546057224274;0.7182884812355042;0.6613323092460632
29;0.9739112257957458;0.06734197586774826;0.8414078950881958;0.41680699586868286
30;0.9771550893783569;0.059989478439092636;0.8821256160736084;0.3568744957447052
31;0.9750845432281494;0.06636033952236176;0.8358868360519409;0.5067021250724792
32;0.9767409563064575;0.059949979186058044;0.8089717030525208;0.48687830567359924
33;0.9798467755317688;0.052973438054323196;0.8895789980888367;0.4174205958843231
34;0.9806404709815979;0.05198125168681145;0.8462387919425964;0.43663835525512695
35;0.9809165596961975;0.050926707684993744;0.8792270421981812;0.4253958463668823
36;0.9822969436645508;0.0489950068295002;0.8697032332420349;0.41362133622169495
37;0.9808130264282227;0.04980352520942688;0.8698412775993347;0.5531616806983948
38;0.9826074838638306;0.04674884304404259;0.6884748339653015;0.9440109133720398
39;0.981468677520752;0.04753546416759491;0.8129744529724121;0.7017194032669067
40;0.9834011793136597;0.04391661286354065;0.8926156163215637;0.4125475585460663
41;0.9871281385421753;0.03651078790426254;0.7420290112495422;0.7097376585006714
42;0.9820208549499512;0.04804307222366333;0.8721877336502075;0.3985663950443268
43;0.9867485761642456;0.03585134074091911;0.7363699078559875;0.7428156733512878
44;0.9859203696250916;0.03767611086368561;0.8797791600227356;0.48716306686401367
45;0.9873006939888;0.034870341420173645;0.7378882169723511;0.8518052101135254
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47;0.9857133030891418;0.0370221845805645;0.882539689540863;0.4600583612918854
48;0.9879909157752991;0.03265931084752083;0.8458247184753418;0.5212200284004211
49;0.9854372143745422;0.038105424493551254;0.8818495273590088;0.4397183358669281
0;0.6683000922203064;0.5881448984146118;0.7316769957542419;0.6395809650421143
1;0.7852509021759033;0.450373113155365;0.7940648794174194;0.4377261996269226
2;0.8124439120292664;0.4062140882015228;0.8002760410308838;0.4373267889022827
3;0.8277658820152283;0.3763357400894165;0.787025511264801;0.47278499603271484
4;0.8419835567474365;0.3512009382247925;0.8238785266876221;0.3859238624572754
5;0.854130744934082;0.3272089958190918;0.7893719673156738;0.5236796736717224
6;0.8704534769058228;0.2996101677417755;0.8060731291770935;0.42027977108955383
7;0.8825660943984985;0.2743792235851288;0.6970324516296387;0.637311577796936
8;0.8920905590057373;0.24966645240783691;0.7660455703735352;0.49229806661605835
9;0.9030298590660095;0.23076249659061432;0.7910283207893372;0.5062369108200073
10;0.9131755232810974;0.20935359597206116;0.8129744529724121;0.46490365266799927
11;0.9230105876922607;0.18994265794754028;0.866252601146698;0.3232031762599945
12;0.9277728199958801;0.17517268657684326;0.8109040856361389;0.4511100947856903
13;0.9337428212165833;0.1634548306465149;0.7550034523010254;0.6370905041694641
14;0.938919186592102;0.15161541104316711;0.8648723363876343;0.3315387964248657
15;0.9445441365242004;0.13732337951660156;0.8782608509063721;0.31262508034706116
16;0.9471668004989624;0.1306610256433487;0.7014492750167847;0.7027690410614014
17;0.954206645488739;0.1191878542304039;0.8835058808326721;0.2979317605495453
18;0.9569673538208008;0.11105921119451523;0.5994479060173035;1.1885627508163452
19;0.9586237668991089;0.10211963951587677;0.8741200566291809;0.358771413564682
20;0.960107684135437;0.09852218627929688;0.8788129687309265;0.3162361681461334
21;0.9658016562461853;0.08837369829416275;0.8859903216362;0.34624937176704407
22;0.9674925804138184;0.0853009894490242;0.886956512928009;0.3779943585395813
23;0.9679412245750427;0.08097299933433533;0.8547964096069336;0.3819237947463989
24;0.9713920950889587;0.07439553737640381;0.8926156163215637;0.3269212245941162
25;0.973773181438446;0.06952481716871262;0.818495512008667;0.5213053226470947
26;0.9746014475822449;0.06690952181816101;0.8121463060379028;0.5251126289367676
27;0.9756711721420288;0.06301873177289963;0.6492753624916077;1.1995936632156372
28;0.9773276448249817;0.061844486743211746;0.7694962024688721;0.6478196382522583
29;0.9791221022605896;0.05358189716935158;0.8800551891326904;0.41321438550949097
30;0.9794671535491943;0.054509732872247696;0.883781909942627;0.383851557970047
31;0.9795016646385193;0.05316682532429695;0.8895789980888367;0.40091606974601746
32;0.9816757440567017;0.04722513258457184;0.8897170424461365;0.4192144572734833
33;0.9817102551460266;0.050145361572504044;0.8840579986572266;0.41027480363845825
34;0.9829180836677551;0.04515507444739342;0.8372671008110046;0.5511378049850464
35;0.9834356904029846;0.04553219676017761;0.8603174686431885;0.513424277305603
36;0.9851611852645874;0.040779467672109604;0.8334023356437683;0.6079146265983582
37;0.9845744967460632;0.04306085407733917;0.8191856741905212;0.637920618057251
38;0.9845399856567383;0.04231315478682518;0.8422360420227051;0.6051537394523621
39;0.985609769821167;0.03996659815311432;0.7810903787612915;0.7231553196907043
40;0.9864034652709961;0.03687221184372902;0.8939958810806274;0.44444236159324646
41;0.9874387383460999;0.033657826483249664;0.8064872622489929;0.6591091156005859
42;0.9886120557785034;0.03210468217730522;0.8819875717163086;0.4260459542274475
43;0.9859893918037415;0.03774571046233177;0.8991028070449829;0.4246087968349457
44;0.9898889064788818;0.028389202430844307;0.6928916573524475;1.30232834815979
45;0.9860239028930664;0.037385717034339905;0.8974465131759644;0.42504382133483887
46;0.9884395003318787;0.03266832232475281;0.8928916454315186;0.4237724244594574
47;0.9909241199493408;0.0245486069470644;0.7472739815711975;0.9512647986412048
48;0.987335205078125;0.03453516960144043;0.8367149829864502;0.5815234780311584
49;0.9895783066749573;0.028951218351721764;0.8623878359794617;0.5441285371780396
best_model_train_loss,best_model_val_loss,best_model_train_acc,best_model_val_acc
0.04306085407733917,0.22768545150756836,0.9845744967460632,0.9108350586611457
loss,accuracy,val_loss,val_accuracy
0.5881448984146118,0.6683000922203064,0.542445182800293,0.7456176673567978
0.450373113155365,0.7852509021759033,0.43849435448646545,0.8031746031746032
0.4062140882015228,0.8124439120292664,0.40914323925971985,0.8178053830227743
0.3763357400894165,0.8277658820152283,0.4021022617816925,0.8220841959972395
0.3512009382247925,0.8419835567474365,0.37622183561325073,0.8354727398205659
0.3272089958190918,0.854130744934082,0.37701696157455444,0.8353347135955832
0.2996101677417755,0.8704534769058228,0.36067256331443787,0.8454106280193237
0.2743792235851288,0.8825660943984985,0.4189978539943695,0.8372670807453416
0.24966645240783691,0.8920905590057373,0.3443162441253662,0.8618357487922705
0.23076249659061432,0.9030298590660095,0.3187393546104431,0.8677708764665286
0.20935359597206116,0.9131755232810974,0.31983938813209534,0.8611456176673568
0.18994265794754028,0.9230105876922607,0.2846546769142151,0.8865424430641822
0.17517268657684326,0.9277728199958801,0.2940806746482849,0.8810213940648723
0.1634548306465149,0.9337428212165833,0.2924601435661316,0.8777087646652864
0.15161541104316711,0.938919186592102,0.2642154097557068,0.8930296756383713
0.13732337951660156,0.9445441365242004,0.2618846893310547,0.8979986197377502
0.1306610256433487,0.9471668004989624,0.3324805796146393,0.8766045548654244
0.1191878542304039,0.954206645488739,0.28171733021736145,0.8944099378881988
0.11105921119451523,0.9569673538208008,0.30583834648132324,0.8930296756383713
0.10211963951587677,0.9586237668991089,0.3100980520248413,0.8726017943409248
0.09852218627929688,0.960107684135437,0.3086910545825958,0.8837819185645273
0.08837369829416275,0.9658016562461853,0.21954958140850067,0.9089026915113871
0.0853009894490242,0.9674925804138184,0.27418917417526245,0.9011732229123534
0.08097299933433533,0.9679412245750427,0.25278550386428833,0.9002070393374741
0.07439553737640381,0.9713920950889587,0.2572042644023895,0.8984126984126984
0.06952481716871262,0.973773181438446,0.3096548914909363,0.8864044168391995
0.06690952181816101,0.9746014475822449,0.2369784563779831,0.904071773636991
0.06301873177289963,0.9756711721420288,0.3666507601737976,0.8506556245686681
0.061844486743211746,0.9773276448249817,0.33761829137802124,0.8742581090407178
0.05358189716935158,0.9791221022605896,0.29987868666648865,0.8895790200138026
0.054509732872247696,0.9794671535491943,0.22794266045093536,0.9095928226363009
0.05316682532429695,0.9795016646385193,0.24974025785923004,0.9089026915113871
0.04722513258457184,0.9816757440567017,0.2672804296016693,0.893167701863354
0.050145361572504044,0.9817102551460266,0.29554009437561035,0.8971704623878537
0.04515507444739342,0.9829180836677551,0.3161299228668213,0.8732919254658386
0.04553219676017761,0.9834356904029846,0.24429000914096832,0.904623878536922
0.040779467672109604,0.9851611852645874,0.25079500675201416,0.8967563837129054
0.04306085407733917,0.9845744967460632,0.22768545150756836,0.9108350586611457
0.04231315478682518,0.9845399856567383,0.34011775255203247,0.853416149068323
0.03996659815311432,0.985609769821167,0.27293506264686584,0.8870945479641131
0.03687221184372902,0.9864034652709961,0.2515823245048523,0.9064182194616978
0.033657826483249664,0.9874387383460999,0.3467876613140106,0.8687370600414078
0.03210468217730522,0.9886120557785034,0.25719019770622253,0.8966183574879227
0.03774571046233177,0.9859893918037415,0.32062387466430664,0.8756383712905452
0.028389202430844307,0.9898889064788818,0.29805707931518555,0.8926155969634231
0.037385717034339905,0.9860239028930664,0.2604907751083374,0.9060041407867495
0.03266832232475281,0.9884395003318787,0.3244354724884033,0.8636300897170462
0.0245486069470644,0.9909241199493408,0.2839159071445465,0.9032436162870946
0.03453516960144043,0.987335205078125,0.27999794483184814,0.8910973084886128
0.028951218351721764,0.9895783066749573,0.3571220636367798,0.8396135265700483
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: 6817.10343337059 seconds ---
INFO:root:Finished Logging
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