Python解析MNIST数据集

#coding=UTF-8import numpy as npimport structimport matplotlib.pyplot as pltdef parese_idx3(idx3_file):    """    idx3文件解析方法    :param idx3_file: idx3文件路径    :return: 数据集    """    # 读取二进制数据    bin_data = open(idx3_file, 'rb').read()    # 解析文件头信息 magic、imgs、height、width    # '>IIII'是说使用大端法读取4个unsinged int32    offset = 0    fmt_header = '>iiii'    magic, imgs, height, width = struct.unpack_from(fmt_header, bin_data, offset)    print ('magic:%d, imgs: %d, heightXwidth: %dX%d' % (magic, imgs, height, width))    # 解析数据集    image_size = height * width    offset += struct.calcsize(fmt_header)    fmt_image = '>' + str(image_size) + 'B'    images = np.empty((imgs, height, width))    for i in range(imgs):        if (i + 1) % 10000 == 0:            print ('已解析 %d' % (i + 1) + '张');        images[i] = np.array(struct.unpack_from(fmt_image, bin_data, offset)).reshape((height, width))        offset += struct.calcsize(fmt_image)    return imagesdef parese_idx1(idx1_file):    """    idx1文件解析方法    :param idx1_file: idx1文件路径    :return: 数据集    """    # 读取二进制数据    bin_data = open(idx1_file, 'rb').read()    # 解析文件头信息 magic、imgs    offset = 0    fmt_header = '>ii'    magic, imgs = struct.unpack_from(fmt_header, bin_data, offset)    print ('magic:%d, imgs: %d' % (magic, imgs))    # 解析数据集    offset += struct.calcsize(fmt_header)    fmt_image = '>B'    labels = np.empty(imgs)    for i in range(imgs):        if (i + 1) % 10000 == 0:            print ('已解析 %d' % (i + 1) + '张')        labels[i] = struct.unpack_from(fmt_image, bin_data, offset)[0]        offset += struct.calcsize(fmt_image)    return labelsimgs = parese_idx3("ubyte/t10k-images.idx3-ubyte");labs = parese_idx1("ubyte/t10k-labels.idx1-ubyte");for i in range(10):    print(labs[i])    plt.imshow(imgs[i])    plt.show()

PS:
t10k-images.idx3-ubyte = img1
t10k-labels.idx1-ubyte = lab1
lab1是img1的标签信息