可视化学习笔记9-pytorch cifar10数据可视化，归一化可视化。_cifar10数据集下载可视化

cifar10数据可视化

import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
import torch
import torchvision
import torchvision.transforms as transforms


#下载数据预处理
transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
trainset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform )
trainloader=torch.utils.data.DataLoader(trainset,batch_size=5,shuffle=True,num_workers=0)
testset=torchvision.datasets.CIFAR10(root='./data',train=False,download=False,transform=transform )
testloader=torch.utils.data.DataLoader(testset,batch_size=5,shuffle=False)

classes=('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')#10个类

#可视化
import matplotlib.pyplot as plt
import numpy as np

def imshow(img):
    img=img/2+0.5
    npimg=img.numpy()
    plt.imshow(np.transpose(npimg,(1,2,0)))
    plt.savefig('./img1.png')
    plt.show()

    #随机获取部分训练数据
dataiter=iter(testloader)  #依次取出迭代器里的值。执行一次只能取到迭代器里的一个值
images,labels=dataiter.next()
#显示图像
imshow(torchvision.utils.make_grid(images))
print(''.join('%5s'% classes[labels[j]] for j in range(4) ))

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import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np
torch.cuda.set_device(0)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

batch_size = 40
Epochs = 250


trans = torchvision.transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
cifar_10 = torchvision.datasets.CIFAR10(root='./data', train=True, transform=trans, download=False)
data_loader = DataLoader(cifar_10, batch_size=batch_size, shuffle=True)

def imshow(img):

    #反归一化，将数据重新映射到0-1之间
    img = img / 2 + 0.5
    plt.figure(figsize=(10, 4))
    plt.imshow(np.transpose(img.numpy(), (1,2,0)))
    plt.show()


for i, (images, _) in enumerate(data_loader):

    print(i)
    print(images.numpy().shape)
    # plt.subplot(4, 10, images + 1)
    imshow(torchvision.utils.make_grid(images))
    break
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