Driver Drowsiness Detection

CNN_model.py

def driver_drowsiness_detection_model(input_shape=(32, 32, 3)):
    model = Sequential()
    model.add(Input(shape=input_shape))
    model.add(Conv2D(32, (3, 3), padding='same', strides=(1, 1), name='conv1', activation='relu', 
                     kernel_initializer=glorot_uniform(seed=0)))
    model.add(BatchNormalization())
    model.add(Conv2D(32, (3, 3), padding='same', strides=(1, 1), name='conv2', activation='relu', 
                     kernel_initializer=glorot_uniform(seed=0)))
    model.add(BatchNormalization())
    model.add(Dropout(0.2))
    model.add(MaxPool2D((2, 2), strides=(2, 2)))
    
    model.add(Conv2D(64, (3, 3), padding='same', strides=(1, 1), name='conv3', activation='relu', 
                     kernel_initializer=glorot_uniform(seed=0)))
    model.add(BatchNormalization())
    model.add(MaxPool2D((2, 2), strides=(2, 2)))
    model.add(Conv2D(64, (3, 3), padding='same', strides=(1, 1), name='conv4', activation='relu', 
                     kernel_initializer=glorot_uniform(seed=0)))
    model.add(BatchNormalization())
    model.add(Dropout(0.3))
    model.add(MaxPool2D((2, 2), strides=(2, 2)))
    
    model.add(Conv2D(64, (3, 3), padding='same', strides=(1, 1), name='conv5', activation='relu', 
                     kernel_initializer=glorot_uniform(seed=0)))
    model.add(BatchNormalization())
    model.add(Conv2D(64, (3, 3), padding='same', strides=(1, 1), name='conv6', activation='relu', 
                     kernel_initializer=glorot_uniform(seed=0)))
    model.add(BatchNormalization())
    model.add(Conv2D(64, (3, 3), padding='same', strides=(1, 1), name='conv7', activation='relu', 
                     kernel_initializer=glorot_uniform(seed=0)))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))
    model.add(MaxPool2D((2, 2), strides=(2, 2)))
    
    
    model.add(Flatten())
    model.add(Dense(128, activation='relu', kernel_initializer=glorot_uniform(seed=0), name='fc1'))
    model.add(Dropout(0.5))
    model.add(Dense(128, activation='relu', kernel_initializer=glorot_uniform(seed=0), name='fc2'))
    model.add(Dropout(0.5))
    model.add(Dense(2, activation='softmax', kernel_initializer=glorot_uniform(seed=0), name='fc3'))
    
    optimizer = Adam(0.0001)
    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
    return model