Training an image classifier训练图像分类器

本节没有写在GPU和多个GPU上训练的代码，主要写了训练图像分类器，分为5个步骤

一、Loading and normalizing CIFAR10 加载并标准化CIFAR10

import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

注意：
If running on Windows and you get a BrokenPipeError, try setting the num_worker of torch.utils.data.DataLoader() to 0.
如果在Windows上运行时遇到BrokenPipeError，请尝试设置torch.utils.data.DataLoader（）为0。

下载训练集和测试集

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=False, 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=False, 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')

二、Define a Convolutional Neural Network 定义卷积神经网络

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

接下来的所有代码都将在if name == ‘main’:中进行抒写，原因可参考点击查看文章

三、Define a Loss function and optimizer 定义损失函数和优化器

四、Train the network训练网络

五、Test the network on the test data 在测试数据上测试网络

if __name__ == '__main__':# 展示一些训练图像# functions to show an imagedef imshow(img):img = img / 2 + 0.5     # unnormalizenpimg = img.numpy()plt.imshow(np.transpose(npimg, (1, 2, 0)))plt.show()# get some random training imagesdataiter = iter(trainloader)images, labels = dataiter.next()# show imagesimshow(torchvision.utils.make_grid(images))# print labelsprint('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))net = Net()# 3.Define a Loss function and optimizer使用分类交叉熵损失和带有动量的SGDcriterion = nn.CrossEntropyLoss()optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)"""训练完后保存注释掉，方便后续代码的执行# 4.Train the network训练网络for epoch in range(5):  # loop over the dataset multiple timesrunning_loss = 0.0for i, data in enumerate(trainloader, 0):# get the inputs; data is a list of [inputs, labels]inputs, labels = data# zero the parameter gradientsoptimizer.zero_grad()# forward + backward + optimizeoutputs = net(inputs)loss = criterion(outputs, labels)loss.backward()optimizer.step()# print statisticsrunning_loss += loss.item()if i % 2000 == 1999:  # print every 2000 mini-batchesprint('[%d, %5d] loss: %.3f' %(epoch + 1, i + 1, running_loss / 2000))running_loss = 0.0print('Finished Training')PATH = './cifar_net.pth'torch.save(net.state_dict(), PATH)"""# 5. Test the network on the test data在测试数据上测试网络# 首先第一步显示测试集中的图像以使其熟悉 32-47# 第二步重新加载保存的模型 71-104outputs = net(images)_, predicted = torch.max(outputs, 1)print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(4)))# 查看网络在整个数据集上的表现correct = 0total = 0with torch.no_grad():for data in testloader:images, labels = dataoutputs = net(images)_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item()print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))# 哪些类的表现良好，哪些类的表现不佳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 = dataoutputs = 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] += 1for i in range(10):print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))