Model.py

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Activation, Permute, Dropout
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from tensorflow.keras.layers import SeparableConv2D, DepthwiseConv2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import SpatialDropout2D
from tensorflow.keras.layers import Input, Flatten
from tensorflow.keras.constraints import max_norm
from tensorflow.keras import backend as K

def EEGNet(nb_classes, Chans = 64, Samples = 128, 
             dropoutRate = 0.5, kernLength = 64, F1 = 8, 
             D = 2, F2 = 16, norm_rate = 0.25):
    '''	
        Create an EEGNet Model with the given parameters
        see for original implementation :
            https://github.com/vlawhern/arl-eegmodels
        --nb_classes : number of classes and number of neurons in the last layer 
        --Chans : number of data channels , 
        --Samples : number of sample during time  
        --dropoutRate  
        --kernLength  
        --F1 : number of filter in the first convolutional layer
        --D  : depth multiplier in the depthwise layer 
        --F2 : number of filter in the second convolutional layer 
        --norm_rate : normalization rate 
        Return:	EEGNet Tensorflow model
	'''
    dropoutType = Dropout
    input1   = Input(shape = (Chans, Samples, 1))
    ##################################################################
    block1       = Conv2D(F1, (1, kernLength), padding = 'same',
                                   input_shape = (Chans, Samples, 1),
                                   use_bias = False)(input1)
    block1       = BatchNormalization()(block1)
    block1       = DepthwiseConv2D((Chans, 1), use_bias = False, 
                                   depth_multiplier = D,
                                   depthwise_constraint = max_norm(1.))(block1)
    block1       = BatchNormalization()(block1)
    
    block1       = Activation('elu')(block1)
    block1       = AveragePooling2D((1, 4))(block1)
    block1       = dropoutType(dropoutRate)(block1)
    
    block2       = SeparableConv2D(F2, (1, 16),
                                   use_bias = False, padding = 'same')(block1)
    block2       = BatchNormalization()(block2)
    block2       = Activation('elu')(block2)
    block2       = AveragePooling2D((1, 8))(block2)
    block2       = dropoutType(dropoutRate)(block2)
        
    flatten      = Flatten(name = 'flatten')(block2)
    
    dense        = Dense(nb_classes, name = 'dense', 
                         kernel_constraint = max_norm(norm_rate))(flatten)
    softmax      = Activation('softmax', name = 'softmax')(dense)
    
    return Model(inputs=input1, outputs=softmax)