利用pytorch实现对CIFAR-10数据集的分类

框架(架构)  /  管理员 发布于 7年前   435

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：

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转成Image，方便可视化import matplotlib.pyplot as pltimport torchvisionimport 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()print(images.size())imshow(torchvision.utils.make_grid(images))plt.show()#关掉图片才能往后继续算#########################定义网络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)#############定义损失函数和优化器from torch import optimcriterion=nn.CrossEntropyLoss()optimizer=optim.SGD(net.parameters(),lr=0.01,momentum=0.9)##############训练网络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):    #输入数据    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()    #更新参数    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)

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