任务描述：

通过简单的自定义神经网络，实现CIFAR10数据集图像分类任务

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
import torch.utils.data
import torchvision
import torchvision.transforms as transforms
import torch.optim as optimimport matplotlib.pyplot as plt
import numpy as npdef show_img(img):"""显示图片"""img = img/2 + 0.5npimg = img.numpy()plt.imshow(np.transpose(npimg, (1,2,0)))plt.show()# torchvision输出的是PILImage, 值的范围是[0, 1]
# 我们将其转化为张量数据， 并归一化为[-1, 1]
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
])# 下载训练集
trainset = torchvision.datasets.CIFAR10(root= "./data",train= True,download=True,transform=transform
)trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=False, num_workers=2)
classes = ["plane", "car", "brid", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]class Net(nn.Module):def __init__(self):super(Net, self).__init__()# 输入为3通道, 输出为6通道, 卷积核为5self.conv1 = nn.Conv2d(3,6,5)# 输入为6通道，输出为16通道，卷积核为5self.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(-1, self.num_flat_features(x))x = F.relu(self.fc1(x))x = F.relu(self.fc2(x))x = self.fc3(x)return xdef num_flat_features(self, x):size = x.size()[1:]num_features = 1for s in size:num_features *= sreturn num_featuresnet = Net()
# 交叉熵损失函数
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)if __name__ == "__main__":for epoch in range(1):running_loss = 0.0for i,data in enumerate(trainloader, 0):inputs, labels = data# 梯度清零optimizer.zero_grad()outputs = net(inputs)loss = criterion(outputs, labels)loss.backward()optimizer.step()running_loss += loss.item()if i%2000 == 1999:print(epoch+1, i+1, running_loss/2000)running_loss = 0print("Finished Training")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)correct = 0total = 0 with torch.no_grad():for data in testloader:images, labels = dataoutputs = net(images)value, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted==labels).sum()print(correct/total)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(classes[i], 100*class_correct[i]/class_total[i])