最近在学习Pytorch,先照着别人的代码过一遍,加油!!!
加载数据集
# 加载数据集及预处理 import torchvision as tv import torchvision.transforms as transforms from torchvision.transforms import ToPILImage import torch as t show=ToPILImage() #可以将Tensor转成Image,方便可视化
划分数据集为训练集和测试集
#定义对数据的预处理 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='/home/cy/data', train=True, download=True, transform=transform ) trainloader=t.utils.data.DataLoader( trainset, batch_size=4, shuffle=True, num_workers=2 ) testset=tv.datasets.CIFAR10( '/home/cy/data/', train=False, download=True, transform=transform ) testloader=t.utils.data.DataLoader( testset, batch_size=4, shuffle=False, num_workers=2 ) classes=('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')
Files already downloaded and verified Files already downloaded and verified
可视化看下图片效果
(data, label)=trainset[100] print(classes[label]) #(data+1)是为了还原被归一化的数据 show((data+1)/2).resize((100,100))
展示一个mini-batch中的图片
dataiter=iter(trainloader) images,labels=dataiter.next() #返回4张图片及标签 print(' '.join('%11s'%classes[labels[j]] for j in range(4))) show(tv.utils.make_grid((images+1)/2)).resize((400,100))
定义网络结构,挺方便的
## 定义网络 import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net,self).__init__() self.conv1=nn.Conv2d(3,6,5) self.conv2=nn.Conv2d(6,16,5) self.fc1=nn.Linear(16*5*5,120) self.fc2=nn.Linear(120,84) self.fc3=nn.Linear(84,10) def forward(self,x): x=F.max_pool2d(F.relu(self.conv1(x)),(2,2)) x=F.max_pool2d(F.relu(self.conv2(x)),2) x=x.view(x.size()[0],-1) x=F.relu(self.fc1(x)) x=F.relu(self.fc2(x)) x=self.fc3(x) return x net=Net() print(net)
Net( (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1)) (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1)) (fc1): Linear(in_features=400, out_features=120, bias=True) (fc2): Linear(in_features=120, out_features=84, bias=True) (fc3): Linear(in_features=84, out_features=10, bias=True) )
定义损失函数和优化器
## 定义损失函数和优化器 from torch import optim criterion=nn.CrossEntropyLoss() # 交叉熵损失函数 optimizer=optim.SGD(net.parameters(),lr=0.001,momentum=0.9) #随机梯度下降,stochastic gradient descent
开始训练网络
一共有三个步骤。输入数据,前向传播+反向传播,更新参数
from torch.autograd import Variable for epoch in range(2): running_loss=0.0 for i,data in enumerate(trainloader,0): #输入数据 inputs,labels=data inputs,labels=Variable(inputs),Variable(labels) #梯度清零 optimizer.zero_grad() #forward+backward outputs=net(inputs) loss=criterion(outputs,labels) loss.backward() #更新参数 optimizer.step() #打印log信息 #running_loss +=loss.data[0] running_loss +=loss.item() if i%2000 ==1999: #每2000个batch打印一次训练状态 print('[%d, %5d] loss: %.3f' \ %(epoch+1,i+1,running_loss / 2000)) running_loss=0.0 print('Finished Training')
检查一下网络在一个batch内的效果如何
## 检验网络效果 dataiter=iter(testloader) images,labels=dataiter.next() #一个batch返回4张图片 print('实际的label: ',' '.join(\ '%08s'%classes[labels[j]] for j in range(4))) show(tv.utils.make_grid(images/2 -0.5)).resize((400,100)) # 计算网络预测的label outputs=net(Variable(images)) _,predicted=t.max(outputs.data,1) print('预测结果: ',' '.join('%5s'\ % classes[predicted[j]] for j in range(4)))
测试集上计算正确率
correct=0 total=0 for data in testloader: images,labels=data outputs=net(Variable(images)) _,predicted=t.max(outputs.data,1) total +=labels.size(0) correct +=(predicted==labels).sum() print('1000张测试集中的准确率为: %d %%' %(100* correct/total))
1000张测试集中的准确率为: 52 %
可以看到,在CIFAR-10上的正确率为52%,网络训练还是有些效果的。
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