PyTorch实战福利从入门到精通之四——卷积神经网络CIFAR-10图像分类

在本教程中，我们将使用CIFAR10数据集。它有类别:“飞机”、“汽车”、“鸟”、“猫”、“鹿”、“狗”、“青蛙”、“马”、“船”、“卡车”。CIFAR-10中的图像大小为3x32x32，即3通道彩色图像大小为32x32像素。

我们将按以下顺序进行:
1.使用torchvision加载和规范CIFAR10培训和测试数据集
2.定义一个卷积神经网络
3.定义损失函数
4.根据培训数据对网络进行培训
5.在测试数据上测试网络 

1.

import torch
import torchvision
import torchvision.transforms as transforms

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

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

Out:

Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
Extracting ./data/cifar-10-python.tar.gz to ./data
Files already downloaded and verified

import matplotlib.pyplot as plt
import numpy as np

# functions to show an image


def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()


# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))

Out:

car  deer  bird   car

2.

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.pool = nn.MaxPool2d(2, 2)
        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 = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        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()

3.

import torch.optim as optim

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

4.

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')



Out:

[1,  2000] loss: 2.247
[1,  4000] loss: 1.899
[1,  6000] loss: 1.702
[1,  8000] loss: 1.574
[1, 10000] loss: 1.504
[1, 12000] loss: 1.489
[2,  2000] loss: 1.401
[2,  4000] loss: 1.391
[2,  6000] loss: 1.353
[2,  8000] loss: 1.332
[2, 10000] loss: 1.309
[2, 12000] loss: 1.291
Finished Training

保存训练的模型

PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)

5.

class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1


for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))



Out:

Accuracy of plane : 62 %
Accuracy of   car : 66 %
Accuracy of  bird : 40 %
Accuracy of   cat : 42 %
Accuracy of  deer : 59 %
Accuracy of   dog : 32 %
Accuracy of  frog : 46 %
Accuracy of horse : 64 %
Accuracy of  ship : 75 %
Accuracy of truck : 63 %

 就像你把一个张量转移到GPU上一样，你把神经网络转移到GPU上。
让我们首先定义我们的设备为第一个可见的cuda设备，如果我们有cuda可用:

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Assuming that we are on a CUDA machine, this should print a CUDA device:

print(device)




Out:

cuda:0

 然后这些方法将递归遍历所有模块，并将其参数和缓冲区转换为CUDA张量，也要记得把输入转移到GPU

net.to(device)
inputs, labels = data[0].to(device), data[1].to(device)

会发现GPU提速不大，这是因为网络规模小