Merged conflicts

EEGNet/eegNet.py

@@ -18,16 +18,14 @@ import logging
 from sklearn.model_selection import train_test_split
 
 class prediction_history(tf.keras.callbacks.Callback):
-    def __init__(self, val_data):
-        self.val_data = val_data
+    def __init__(self, validation_data):
+        self.validation_data = validation_data
         self.predhis = []
-        self.targets = []
+        self.targets = validation_data[1]
 
-    def on_batch_end(self, epoch, logs={}):
-        x_val, y_val = self.val_data
-        self.targets.append(y_val)
-        prediction = self.model.predict(x_val)
-        self.predhis.append(prediction)
+    def on_epoch_end(self, epoch, logs={}):
+        y_pred = self.model.predict(self.validation_data[0])
+        self.predhis.append(y_pred)
 
 class Classifier_EEGNet:
     """

PyramidalCNN/PyramidalCNN.py

+import tensorflow as tf
+from config import config
+from utils.utils import *
+import logging
+from ConvNet import ConvNet
+from tensorflow.keras.constraints import max_norm
+
+
+class Classifier_PyramidalCNN(ConvNet):
+    """
+    The Classifier_PyramidalCNN is one of the simplest classifiers. It implements the class ConvNet, which is made of modules with a
+    specific depth, where for each depth the number of filters is increased.
+    """
+    def __init__(self, input_shape, kernel_size=16, epochs = 50, nb_filters=16, verbose=True, batch_size=64, use_residual=False, depth=6):
+
+        super(Classifier_PyramidalCNN, 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.
+        """
+        x = tf.keras.layers.Conv1D(filters=self.nb_filters*(current_depth + 1), 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(pool_size=2, strides=2)(x)
+        return x

config.py

@@ -42,7 +42,7 @@ Cluster can be set to clustering(), clustering2() or clustering3(), where differ
 """
 
 # Choosing model
-config['model'] = 'eegnet'
+config['model'] = 'pyramidal_cnn'
 config['downsampled'] = False
 config['split'] = False
 config['cluster'] = clustering()
@@ -57,6 +57,8 @@ config['trainY_variable'] = 'all_trialinfoprosan'
 
 # CNN
 config['cnn'] = {}
+# CNN
+config['pyramidal_cnn'] = {}
 # InceptionTime
 config['inception'] = {}
 # DeepEye
@@ -69,6 +71,7 @@ config['eegnet'] = {}
 config['deepeye-rnn'] = {}
 
 config['cnn']['input_shape'] = (125, 129) if config['downsampled'] else (500, 129)
+config['pyramidal_cnn']['input_shape'] = (125, 129) if config['downsampled'] else (500, 129)
 config['inception']['input_shape'] = (125, 129) if config['downsampled'] else (500, 129)
 config['deepeye']['input_shape'] = (125, 129) if config['downsampled'] else (500, 129)
 config['deepeye-rnn']['input_shape'] = (125, 129) if config['downsampled'] else (500, 129)

ensemble.py

 import tensorflow as tf
+
 from config import config
 from utils.utils import *
 import logging
 
 from CNN.CNN import Classifier_CNN
+from PyramidalCNN.PyramidalCNN import Classifier_PyramidalCNN
 from DeepEye.deepeye import Classifier_DEEPEYE
 from DeepEyeRNN.deepeyeRNN import Classifier_DEEPEYE_RNN
 from Xception.xception import Classifier_XCEPTION
@@ -29,15 +31,19 @@ def run(trainX, trainY):
     for i in range(config['ensemble']):
         print('beginning model number {}/{} ...'.format(i,config['ensemble']))
         if config['model'] == 'deepeye':
-            classifier = Classifier_DEEPEYE(input_shape = config['deepeye']['input_shape'])
+            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'])
+        elif config['model'] == 'pyramidal_cnn':
+            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)
-        elif config['model'] == 'xception' :
+        elif config['model'] == 'xception':
             classifier = Classifier_XCEPTION(input_shape=config['inception']['input_shape'], use_residual=True,
                                               kernel_size=40, nb_filters=64, depth=18, epochs=50)
         elif config['model'] == 'deepeye-rnn':