利用pytorch实现对CIFAR-10数据集的分类_Python

利用pytorch实现对CIFAR-10数据集的分类

2020-04-24 10:41summer2day Python

今天小编就为大家分享一篇利用pytorch实现对CIFAR-10数据集的分类，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

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+pytorch0.3.0

									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()

									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 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)

									    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 x

									net=Net()

									print(net)

									#############定义损失函数和优化器

									from torch import optim

									criterion=nn.CrossEntropyLoss()

									optimizer=optim.SGD(net.parameters(),lr=0.01,momentum=0.9)

									##############训练网络

									from torch.autograd import Variable

									import time

									start_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 = 0.0

									print('finished training')

									end_time = time.time()

									print("Spend time:", end_time - start_time)