Python3读取深度学习CIFAR-10数据集出现的若干问题解决

qinjianhuang 2020-11-13 05:26:14
学习 Python python3 深度 读取


今天在看网上的视频学习深度学习的时候,用到了CIFAR-10数据集。当我兴高采烈的运行代码时,却发现了一些错误:

# -*- coding: utf-8 -*-
import pickle as p
import numpy as np
import os
def load_CIFAR_batch(filename):
""" 载入cifar数据集的一个batch """
with open(filename, 'r') as f:
datadict = p.load(f)
X = datadict['data']
Y = datadict['labels']
X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype("float")
Y = np.array(Y)
return X, Y
def load_CIFAR10(ROOT):
""" 载入cifar全部数据 """
xs = []
ys = []
for b in range(1, 6):
f = os.path.join(ROOT, 'data_batch_%d' % (b,))
X, Y = load_CIFAR_batch(f)
xs.append(X)
ys.append(Y)
Xtr = np.concatenate(xs)
Ytr = np.concatenate(ys)
del X, Y
Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))
return Xtr, Ytr, Xte, Yte

错误代码如下:

'gbk' codec can't decode byte 0x80 in position 0: illegal multibyte sequence

于是乎开始各种搜索问题,问大佬,网上的答案都是类似:

这里写图片描述

然而并没有解决问题!还是错误的!(我大概搜索了一下午吧,都是上面的答案)

哇,就当我很绝望的时候,我终于发现了一个新奇的答案,抱着试一试的态度,尝试了一下:


def load_CIFAR_batch(filename):
""" 载入cifar数据集的一个batch """
with open(filename, 'rb') as f:
datadict = p.load(f, encoding='latin1')
X = datadict['data']
Y = datadict['labels']
X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype("float")
Y = np.array(Y)
return X, Y

竟然成功了,这里没有报错了!欣喜之余,我就很好奇,encoding=’latin1’到底是啥玩意呢,以前没有见过啊?于是,我搜索了一下,了解到:

Latin1是ISO-8859-1的别名,有些环境下写作Latin-1。ISO-8859-1编码是单字节编码,向下兼容ASCII,其编码范围是0x00-0xFF,0x00-0x7F之间完全和ASCII一致,0x80-0x9F之间是控制字符,0xA0-0xFF之间是文字符号。

因为ISO-8859-1编码范围使用了单字节内的所有空间,在支持ISO-8859-1的系统中传输和存储其他任何编码的字节流都不会被抛弃。换言之,把其他任何编码的字节流当作ISO-8859-1编码看待都没有问题。这是个很重要的特性,MySQL数据库默认编码是Latin1就是利用了这个特性。ASCII编码是一个7位的容器,ISO-8859-1编码是一个8位的容器。

还没等我高兴起来,运行后,又发现了一个问题:

memory error

什么鬼?内存错误!哇,原来是数据大小的问题。

X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float")

这告诉我们每批数据都是10000 * 3 * 32 * 32,相当于超过3000万个浮点数。 float数据类型实际上与float64相同,意味着每个数字大小占8个字节。这意味着每个批次占用至少240 MB。你加载6这些(5训练+ 1测试)在总产量接近1.4 GB的数据。

 for b in range(1, 2):
f = os.path.join(ROOT, 'data_batch_%d' % (b,))
X, Y = load_CIFAR_batch(f)
xs.append(X)
ys.append(Y)

所以如有可能,如上代码所示只能一次运行一批。

到此为止,错误基本搞定,下面贴出正确代码:

# -*- coding: utf-8 -*-
import pickle as p
import numpy as np
import os
def load_CIFAR_batch(filename):
""" 载入cifar数据集的一个batch """
with open(filename, 'rb') as f:
datadict = p.load(f, encoding='latin1')
X = datadict['data']
Y = datadict['labels']
X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype("float")
Y = np.array(Y)
return X, Y
def load_CIFAR10(ROOT):
""" 载入cifar全部数据 """
xs = []
ys = []
for b in range(1, 2):
f = os.path.join(ROOT, 'data_batch_%d' % (b,))
X, Y = load_CIFAR_batch(f)
xs.append(X) #将所有batch整合起来
ys.append(Y)
Xtr = np.concatenate(xs) #使变成行向量,最终Xtr的尺寸为(50000,32,32,3)
Ytr = np.concatenate(ys)
del X, Y
Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))
return Xtr, Ytr, Xte, Yte
import numpy as np
from julyedu.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (10.0, 8.0)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# 载入CIFAR-10数据集
cifar10_dir = 'julyedu/datasets/cifar-10-batches-py'
X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)
# 看看数据集中的一些样本:每个类别展示一些
print('Training data shape: ', X_train.shape)
print('Training labels shape: ', y_train.shape)
print('Test data shape: ', X_test.shape)
print('Test labels shape: ', y_test.shape)

顺便看一下CIFAR-10数据组成:

CIFAR-10数据组成


附件:CIFAR-10 python version下载地址

CIFAR-10官网

版权声明
本文为[qinjianhuang]所创,转载请带上原文链接,感谢
https://huangqinjian.blog.csdn.net/article/details/78502782

  1. 利用Python爬虫获取招聘网站职位信息
  2. Using Python crawler to obtain job information of recruitment website
  3. Several highly rated Python libraries arrow, jsonpath, psutil and tenacity are recommended
  4. Python装饰器
  5. Python实现LDAP认证
  6. Python decorator
  7. Implementing LDAP authentication with Python
  8. Vscode configures Python development environment!
  9. In Python, how dare you say you can't log module? ️
  10. 我收藏的有关Python的电子书和资料
  11. python 中 lambda的一些tips
  12. python中字典的一些tips
  13. python 用生成器生成斐波那契数列
  14. python脚本转pyc踩了个坑。。。
  15. My collection of e-books and materials about Python
  16. Some tips of lambda in Python
  17. Some tips of dictionary in Python
  18. Using Python generator to generate Fibonacci sequence
  19. The conversion of Python script to PyC stepped on a pit...
  20. Python游戏开发,pygame模块,Python实现扫雷小游戏
  21. Python game development, pyGame module, python implementation of minesweeping games
  22. Python实用工具,email模块,Python实现邮件远程控制自己电脑
  23. Python utility, email module, python realizes mail remote control of its own computer
  24. 毫无头绪的自学Python,你可能连门槛都摸不到!【最佳学习路线】
  25. Python读取二进制文件代码方法解析
  26. Python字典的实现原理
  27. Without a clue, you may not even touch the threshold【 Best learning route]
  28. Parsing method of Python reading binary file code
  29. Implementation principle of Python dictionary
  30. You must know the function of pandas to parse JSON data - JSON_ normalize()
  31. Python实用案例,私人定制,Python自动化生成爱豆专属2021日历
  32. Python practical case, private customization, python automatic generation of Adu exclusive 2021 calendar
  33. 《Python实例》震惊了,用Python这么简单实现了聊天系统的脏话,广告检测
  34. "Python instance" was shocked and realized the dirty words and advertisement detection of the chat system in Python
  35. Convolutional neural network processing sequence for Python deep learning
  36. Python data structure and algorithm (1) -- enum type enum
  37. 超全大厂算法岗百问百答(推荐系统/机器学习/深度学习/C++/Spark/python)
  38. 【Python进阶】你真的明白NumPy中的ndarray吗?
  39. All questions and answers for algorithm posts of super large factories (recommended system / machine learning / deep learning / C + + / spark / Python)
  40. [advanced Python] do you really understand ndarray in numpy?
  41. 【Python进阶】Python进阶专栏栏主自述:不忘初心,砥砺前行
  42. [advanced Python] Python advanced column main readme: never forget the original intention and forge ahead
  43. python垃圾回收和缓存管理
  44. java调用Python程序
  45. java调用Python程序
  46. Python常用函数有哪些?Python基础入门课程
  47. Python garbage collection and cache management
  48. Java calling Python program
  49. Java calling Python program
  50. What functions are commonly used in Python? Introduction to Python Basics
  51. Python basic knowledge
  52. Anaconda5.2 安装 Python 库(MySQLdb)的方法
  53. Python实现对脑电数据情绪分析
  54. Anaconda 5.2 method of installing Python Library (mysqldb)
  55. Python implements emotion analysis of EEG data
  56. Master some advanced usage of Python in 30 seconds, which makes others envy it
  57. python爬取百度图片并对图片做一系列处理
  58. Python crawls Baidu pictures and does a series of processing on them
  59. python链接mysql数据库
  60. Python link MySQL database