Parkinson_disease_detection

defining_model.py

def parkinson_disease_detection_model(input_shape=(128, 128, 1)):
    regularizer = tf.keras.regularizers.l2(0.001)
    model = Sequential()
    model.add(Input(shape=input_shape))
    model.add(Conv2D(128, (5, 5), padding='same', strides=(1, 1), name='conv1', activation='relu', 
                     kernel_initializer='glorot_uniform', kernel_regularizer=regularizer))
    model.add(MaxPool2D((9, 9), strides=(3, 3)))

    model.add(Conv2D(64, (5, 5), padding='same', strides=(1, 1), name='conv2', activation='relu', 
                     kernel_initializer='glorot_uniform', kernel_regularizer=regularizer))
    model.add(MaxPool2D((7, 7), strides=(3, 3)))
    
    model.add(Conv2D(32, (3, 3), padding='same', strides=(1, 1), name='conv3', activation='relu', 
                     kernel_initializer='glorot_uniform', kernel_regularizer=regularizer))
    model.add(MaxPool2D((5, 5), strides=(2, 2)))

    model.add(Conv2D(32, (3, 3), padding='same', strides=(1, 1), name='conv4', activation='relu', 
                     kernel_initializer='glorot_uniform', kernel_regularizer=regularizer))
    model.add(MaxPool2D((3, 3), strides=(2, 2)))    
    
    model.add(Flatten())
    model.add(Dropout(0.5))
    model.add(Dense(64, activation='relu', kernel_initializer='glorot_uniform', name='fc1'))
    model.add(Dropout(0.5))
    model.add(Dense(2, activation='softmax', kernel_initializer='glorot_uniform', name='fc3'))
    
    optimizer = Adam(3.15e-5)
    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
    return model