步驟如下：
1.使用torchvision加載并預(yù)處理CIFAR-10數(shù)據(jù)集、
2.定義網(wǎng)絡(luò)
3.定義損失函數(shù)和優(yōu)化器
4.訓(xùn)練網(wǎng)絡(luò)并更新網(wǎng)絡(luò)參數(shù)
5.測(cè)試網(wǎng)絡(luò)
運(yùn)行環(huán)境：
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windows+python3.6.3+pycharm+pytorch0.3.0import torchvision as tvimport torchvision.transforms as transformsimport torch as tfrom torchvision.transforms import ToPILImageshow=ToPILImage() #把Tensor轉(zhuǎn)成Image，方便可視化import matplotlib.pyplot as pltimport torchvisionimport numpy as np###############數(shù)據(jù)加載與預(yù)處理transform = transforms.Compose([transforms.ToTensor(),#轉(zhuǎn)為tensor transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),#歸一化 ])#訓(xùn)練集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)#測(cè)試集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()print(images.size())imshow(torchvision.utils.make_grid(images))plt.show()#關(guān)掉圖片才能往后繼續(xù)算#########################定義網(wǎng)絡(luò)import torch.nn as nnimport torch.nn.functional as Fclass 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) x = F.max_pool2d(F.relu(self.conv2(x)),2) x = x.view(-1, 16 * 5 * 5) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return xnet=Net()print(net)#############定義損失函數(shù)和優(yōu)化器from torch import optimcriterion=nn.CrossEntropyLoss()optimizer=optim.SGD(net.parameters(),lr=0.01,momentum=0.9)##############訓(xùn)練網(wǎng)絡(luò)from torch.autograd import Variableimport timestart_time = time.time()for epoch in range(2): running_loss=0.0 for i,data in enumerate(trainloader,0): #輸入數(shù)據(jù) inputs,labels=data inputs,labels=Variable(inputs),Variable(labels) #梯度清零 optimizer.zero_grad() outputs=net(inputs) loss=criterion(outputs,labels) loss.backward() #更新參數(shù) optimizer.step() # 打印log running_loss += loss.data[0] if i % 2000 == 1999: print('[%d,%5d] loss:%.3f' % (epoch + 1, i + 1, running_loss / 2000)) running_loss = 0.0print('finished training')end_time = time.time()print("Spend time:", end_time - start_time) |
以上這篇利用pytorch實(shí)現(xiàn)對(duì)CIFAR-10數(shù)據(jù)集的分類就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持服務(wù)器之家。
原文鏈接：https://blog.csdn.net/summer2day/article/details/79154731
