num_classes = 2
class LeNet(Sequential):
    def __init__(self, input_shape, nb_classes):
        super().__init__()

        self.add(Conv2D(6, kernel_size=(5,5), strides=(1,1), activation='relu',
                        input_shape=input_shape, padding="same")) ------ ①
        self.add(AveragePooling2D(pool_size=(2,2), strides=(2,2), padding='valid')) ------ ②
        self.add(Conv2D(16, kernel_size=(5,5), strides=(1,1), activation='relu',
                        padding='valid'))
        self.add(AveragePooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
        self.add(Flatten())
        self.add(Dense(120, activation='relu'))
        self.add(Dense(84, activation='relu'))
        self.add(Dense(nb_classes, activation='softmax'))

        self.compile(optimizer='adam',
                     loss=categorical_crossentropy,
                     metrics=['accuracy'])