程序解码MNIST数据集并返回两个tuple

最新推荐文章于 2024-08-06 20:28:49 发布

sinat_38178263

最新推荐文章于 2024-08-06 20:28:49 发布

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文章标签： MNIST数据集解包

本文链接：https://blog.youkuaiyun.com/sinat_38178263/article/details/89074844

该博客介绍了如何使用Python解析MNIST数据集的idx3-ubyte和idx1-ubyte文件，包括训练集和测试集的图像及标签。通过gzip打开文件，利用struct模块进行解包，最终返回训练和测试数据的两个tuple。

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#encoding:utf-8
import numpy as np
import struct
import gzip

a = 'D:/src/tensorflow/data_sets/MNIST_data/train-images-idx3-ubyte.gz'
b = 'D:/src/tensorflow/data_sets/MNIST_data/train-labels-idx1-ubyte.gz'
c = 'D:/src/tensorflow/data_sets/MNIST_data/t10k-images-idx3-ubyte.gz'
d = 'D:/src/tensorflow/data_sets/MNIST_data/t10k-labels-idx1-ubyte.gz'

def decode_idx3_ubyte(idx3_ubyte_file):
   fp = gzip.open(idx3_ubyte_file,'rb')
   bin_data = fp.read()

   offset = 0
   fmt_header = '>iiii'
   magic_number,num_images,num_rows,num_cols = struct.unpack_from(fmt_header,bin_data,offset)
   print ('魔数:%d, 图片数量:%d张,图片大小:%d%d' % (magic_number,num_images,num_rows,num_cols))

   image_size = num_rows*num_cols
   offset += struct.calcsize(fmt_header)
   fmt_image = '>'+str(image_size)+'B'
   images = np.empty((num_images,num_rows,num_cols))
   for i in range(num_images):
       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((num_rows,num_cols))
       offset += struct.calcsize(fmt_image)
   return images

def decode_idx1_ubyte(idx1_ubyte_file):
   fp = gzip.open(idx1_ubyte_file,'rb')
   bin_data = fp.read()

   offset = 0
   fmt_header = '>ii'
   magic_number,num_images = struct.unpack_from(fmt_header,bin_data,offset)
   print ('魔数:%d, 图片数量:%d张' % (magic_number,num_images))

   offset += struct.calcsize(fmt_header)
   fmt_image = '>B'
   labels = np.empty(num_images)
   for i in range(num_images):
       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 labels

def load_mnist_data(train_images=a,train_labels=b,test_images=c,test_labels=d):
   """decode the mnist local train and test data."""
   t_images = decode_idx3_ubyte(train_images)
   t_labels = decode_idx1_ubyte(train_labels)
   v_images = decode_idx3_ubyte(test_images)
   v_labels = decode_idx1_ubyte(test_labels)
   return (t_images,t_labels),(v_images,v_labels)