深度学习之 cnn 进行 CIFAR10 分类

深度学习之 cnn 进行 CIFAR10 分类

import torchvision as tv
import torchvision.transforms as transforms
from torchvision.transforms import ToPILImage
show = ToPILImage()
import torch as t
import torch.nn as nn
import torch.nn.functional as F


transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,0.5,0.5), (0.5, 0.5, 0.5)),
])

# 下载数据
trainset = tv.datasets.CIFAR10(root=".",train=True, download=True, transform=transform)
trainloader = t.utils.data.DataLoader(trainset, batch_size=4,shuffle=True, num_workers=2)
testset = tv.datasets.CIFAR10('.', 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')

# 网络
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()


from torch import optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr = 0.001, momentum=0.9)
from torch.autograd import Variable

for epoch in range(2):
    running_loss = 0.0
    for i,data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = Variable(inputs), Variable(labels)
        
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        
        optimizer.step()
        
        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')

# 测试
correct = 0
total = 0
for data in testloader:
    images, labels = data
    outputs = net(Variable(images))
#     print(outputs.data)
    _, predicted = t.max(outputs.data, 1)
    print(outputs.data,_, predicted)
    total += labels.size(0)
    correct += (predicted == labels).sum()

print('10000张测式中: %d %%' % (100 * correct / total) )