machinelearning_notebook/demo_code/3_CNN_CIFAR.py

 
import torch as t
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.autograd import Variable

import torchvision as tv
import torchvision.transforms as transforms
from torchvision.transforms import ToPILImage

show = ToPILImage() # 可以把Tensor转成Image，方便可视化

# 第一次运行程序torchvision会自动下载CIFAR-10数据集，
# 大约100M，需花费一定的时间，
# 如果已经下载有CIFAR-10，可通过root参数指定

# 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
dataset_path = "../data"
trainset = tv.datasets.CIFAR10(
                    root=dataset_path, train=True, download=True, transform=transform)

trainloader = t.utils.data.DataLoader(
                    trainset,
                    batch_size=4,
                    shuffle=True,
                    num_workers=2)

# 测试集
testset = tv.datasets.CIFAR10(
                    root=dataset_path, 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')


# Define the network
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)

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

t.set_num_threads(8)
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]
        if i % 2000 == 1999:  # 每2000个batch打印一下训练状态
            print('[%d, %5d] loss: %.3f' \
                  % (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0
print('Finished Training')

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))


# 计算图片在每个类别上的分数
outputs = net(Variable(images))
# 得分最高的那个类
_, predicted = t.max(outputs.data, 1)

print('预测结果: ', ' '.join('%5s'\
            % classes[predicted[j]] for j in range(4)))


correct = 0 # 预测正确的图片数
total = 0 # 总共的图片数
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('10000张测试集中的准确率为: %d %%' % (100 * correct / total))