在用mnist進行圖片訓練時，我們mnist的格式為.gz格式的，因此訓練的過程並不直觀。在此，將mnist的資料轉變成圖片格式，便於我們觀察資料在訓練過程中的變化。

step 1

將原.gz格式的資料進行解壓

$gunzip train-images-idx3-ubyte.gz

解壓後的文 件

step 2

採用如下程式碼解析圖片，圖片的訓練集與測試集分開存放，圖片名與圖片分開存放，程式碼如下

import numpy as np  
import os  
import cv2  
import struct  

#get the image and lable set    
 

def load_mnist(path, kind='train'):  
    """Load MNIST data from `path`"""  
    labels_path = os.path.join(path, '%s-labels-idx1-ubyte' % kind)  
    images_path = os.path.join(path, '%s-images-idx3-ubyte' % kind)  
    with open(labels_path, 'rb') as lbpath:  
        magic, n = struct.unpack('>II'
 
, lbpath.read(8))  
        labels = np.fromfile(lbpath, dtype=np.uint8)  
    with open(images_path, 'rb') as imgpath:  
        magic, num, rows, cols = struct.unpack(">IIII", imgpath.read(16))  
        images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)  
    return images, labels  

#print the train image number and shape    
 

X_train, y_train = load_mnist('', kind='train')  
print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1])) 

#print the test image number and shape 
X_test, y_test = load_mnist('', kind='t10k')  
print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))  

#save the train image and lable sperately    
count = np.zeros(10)  
nTrain = len(X_train)
train_labels =[]
fo1 = open('./train_lable/train_lable.txt',"w+")  
for i in xrange(nTrain):  
    label = y_train[i]
    train_labels.append(str(label)+'\n')  
    count[label] += 1  
    #filename = './train/' + str(label) + '/' + str(label) + '_' + str(int(count[label])) + '.png'
    filename = 'train_image/' + str(i) + '_' + str(label) + '.png'   
    img = X_train[i].reshape(28,28)  
    cv2.imwrite(filename, img)
for line in train_labels:  
    fo1.write(line)
fo1.close

#save the train image and lable sperately 
count = np.zeros(10)  
nTest = len(X_test)
test_labels = []
fo2 = open('test_lable/test_lable.txt',"w+")
for i in xrange(nTest):  
    label = y_test[i]
    test_labels.append(str(label) + '\n')  
    count[label] += 1  
    filename = 'test_image/' + str(i) + '.png'  
    img = X_test[i].reshape(28,28)  
    cv2.imwrite(filename, img)
for line in test_labels:
    fo2.write(str(line))
fo2.close

step 3 執行結果