CIFAR10基于pytorch卷积神经网络（cnn）实战_cifar10_cnn_exercise.ipynb

 1、导入相关库

import torch
import torch.nn as nn  #神经网络库
import torchvision #下载数据集，图像转换
from torchvision import transforms,datasets
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

2、载入数据

transform = transforms.Compose([ #定义transform
    transforms.ToTensor(),#将图片由numpy类型转换为tensor类型，torch中数据类型为张量，tensor类型
    transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))#归一化，0.5可自定义，范围在【-1，1】、
])
 
trainset = datasets.CIFAR10(root="D:/cifar10",train=True,download=True,transform=transform)#下载训练集，root表示存放路径，train为训练集，trandform表示对数据进行转换
testset = datasets.CIFAR10(root="D:/cifar10",train=False,download=True,transform=transform)#下载测试集，方法与训练集相

3、创建类

classes = {"plane","car","bird","cat","deer","dog","frog",&