在用深度網路訓練時，大部分網路都要求輸入為3通道，而有時現有的資料為單通道的灰度圖，並且尺寸也不符合網路輸入，可用下面的函式轉換，以minist資料集為例。

import numpy as np
from keras.datasets import mnist
from keras.utils import to_categorical

image_size = 224
def load_mnist(image_size):
    (x_train,y_train),(x_test,y_test) = mnist.load_data()
    train_image = [cv2.cvtColor(cv2.resize(img,(image_size,image_size)),cv2.COLOR_GRAY2BGR) for
 
 img in x_train]
    test_image = [cv2.cvtColor(cv2.resize(img,(image_size,image_size)),cv2.COLOR_GRAY2BGR) for img in x_test]    
    train_image = np.asarray(train_image)
    test_image = np.asarray(test_image)
    train_label = to_categorical(y_train)
    test_label = to_categorical(y_test)
    print('finish loading data!'
 
)
    return train_image, train_label, test_image, test_label