Model

def cnn(image_size, num_classes):
    classifier = Sequential()
    classifier.add(Conv2D(4, (3, 3), input_shape=image_size, activation='relu', padding='same'))
    classifier.add(MaxPooling2D(pool_size = (2, 2)))
    classifier.add(Conv2D(8, (2, 2), activation='relu', padding='same'))
    classifier.add(MaxPooling2D(pool_size = (2, 2)))
    classifier.add(Flatten())
    classifier.add(Dense(num_classes, activation = 'softmax'))
    classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
    return classifier
    
neuralnetwork_cnn = cnn(image_size, num_classes)
neuralnetwork_cnn.summary()