利用利用pytorch实现对实现对CIFAR-10数据集的分类数据集的分类
步骤如下:步骤如下:
1.使用torchvision加载并预处理CIFAR-10数据集、
2.定义网络
3.定义损失函数和优化器
4.训练网络并更新网络参数
5.测试网络
运行环境:运行环境:
windows+python3.6.3+pycharm+pytorch0.3.0
import torchvision as tv
import torchvision.transforms as transforms
import torch as t
from torchvision.transforms import ToPILImage
show=ToPILImage() #把Tensor转成Image,方便可视化
import matplotlib.pyplot as plt
import torchvision
import numpy as np
###############数据加载与预处理
transform = transforms.Compose([transforms.ToTensor(),#转为tensor
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),#归一化
])
#训练集
trainset=tv.datasets.CIFAR10(root='/python projects/test/data/',
train=True,
download=True,
transform=transform)
trainloader=t.utils.data.DataLoader(trainset,
batch_size=4,
shuffle=True,
num_workers=0)
#测试集
testset=tv.datasets.CIFAR10(root='/python projects/test/data/',
train=False,
download=True,
transform=transform)
testloader=t.utils.data.DataLoader(testset,
batch_size=4,
shuffle=True,
num_workers=0)
classes=('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')
(data,label)=trainset[100] print(classes[label])
show((data+1)/2).resize((100,100))
# dataiter=iter(trainloader)
# images,labels=dataiter.next()
# print(''.join('11%s'%classes[labels[j]] for j in range(4)))
# show(tv.utils.make_grid(images+1)/2).resize((400,100))
def imshow(img):
img = img / 2 + 0.5
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
dataiter = iter(trainloader)
images, labels = dataiter.next()