Added the results of eegnet with ensemble with parameters tuned by KerasTuner

config.py

@@ -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()

ensemble.py

@@ -37,8 +37,8 @@ def run(trainX, trainY):
         elif config['model'] == 'pyramidal_cnn':
             classifier = Classifier_PyramidalCNN(input_shape=config['cnn']['input_shape'], epochs=50)
         elif config['model'] == 'eegnet':
-            classifier = Classifier_EEGNet(dropoutRate = 0.5, kernLength = 250, F1 = 16,
-                                           D = 4, F2 = 256, norm_rate = 0.5, dropoutType = 'Dropout',
+            classifier = Classifier_EEGNet(dropoutRate = 0.5, kernLength = 64, F1 = 32,
+                                           D = 8, F2 = 512, norm_rate = 0.5, dropoutType = 'Dropout',
                                            epochs = 50)
         elif config['model'] == 'inception':
             classifier = Classifier_INCEPTION(input_shape=config['inception']['input_shape'], use_residual=True,

main.py

@@ -24,9 +24,9 @@ def main():
         trainX = np.transpose(trainX, (0, 2, 1))
         logging.info(trainX.shape)
 
-    tune(trainX,trainY)
+    # tune(trainX,trainY)
 
-    # run(trainX,trainY)
+    run(trainX,trainY)
     # select_best_model()
     # comparison_plot()
 

results/OHBM/eegnet_ensemble/batches.log

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results/OHBM/eegnet_ensemble/info.log

+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: 0.0162s vs `on_train_batch_end` time: 0.0267s). 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: 0.0134s vs `on_train_batch_end` time: 0.0258s). 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
+INFO:root:**********
+
+INFO:root:--- Runtime: 10836.003910303116 seconds ---
+INFO:root:Finished Logging