总体思路

生成数据dataset

使用loader加载dataset

建模

训练

测试

参考：

https://blog.csdn.net/HcViking/article/details/126688941

import numpy

获取数据集

下载cifar10数据

下载地址

http://www.cs.toronto.edu/~kriz/cifar.html

# !wget http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz

解压包

# !tar -zxvf cifar-10-python.tar.gz

文件介绍

总共有六个文件

五个cifar-10-batches-py/data_batch_1 2 3 4 5

一个测试集cifar-10-batches-py/test_batch

分类分别是[“airplane”,“automobile”,“bird”,“cat”,“deer”,“dog”,“frog”,“horse”,“ship”,“truck”]

加载图片数字化信息

def unpickle(file):
    import pickle
    with open(file, 'rb') as fo:
        dict = pickle.load(fo, encoding='bytes')
    return dict

dic=unpickle("./cifar-10-batches-py/data_batch_1")

查看数据信息

dic.keys()

dict_keys([b'batch_label', b'labels', b'data', b'filenames'])

dic.get(b'filenames')[0:10]

[b'leptodactylus_pentadactylus_s_000004.png',
 b'camion_s_000148.png',
 b'tipper_truck_s_001250.png',
 b'american_elk_s_001521.png',
 b'station_wagon_s_000293.png',
 b'coupe_s_001735.png',
 b'cassowary_s_001300.png',
 b'cow_pony_s_001168.png',
 b'sea_boat_s_001584.png',
 b'tabby_s_001355.png']

len(dic.get(b'filenames'))

10000

X = dic[ b'data']
Y = dic[b'labels']

X[5],type(X),X[5].shape

(array([159, 150, 153, ...,  14,  17,  19], dtype=uint8),
 numpy.ndarray,
 (3072,))

## 其他信息-concatenate

nparray=numpy.array([[1,2,3,4,5],[1,4,5,6,7]])
print(nparray)

xx=numpy.concatenate(nparray)
xx

[[1 2 3 4 5]
 [1 4 5 6 7]]





array([1, 2, 3, 4, 5, 1, 4, 5, 6, 7])

数据读取

import os
import numpy as np
import pickle


#读一个批次
def load_cifar_batch(filename):
    with open(filename,'rb') as f:
        data_dict=pickle.load(f,encoding='bytes')
        images =data_dict[b'data']
        labels=data_dict[b'labels']
        
        print("data shape is {0} and type is {1}".format(images.shape,type(images)))
        print("labels shape is {0} and type is {1}".format(len(labels),type(labels)))
  
        # 把3072列分成3个32*32的数据
        images=images.reshape(10000,3,32,32)
        print("after data reshape is {0} and type is {1}".format(images.shape