Pytorch第三方学习框架，自行构建CNN模型对CIFAR-10数据集的样本进行分类，具体任务如下： （1）自行导入数据集，包含训练集和测试集； （2）自行设计数据预处理方式（例如：归一化、标准化等）； （3）设计CNN网络模型在CIFAR-10数据集的训练集上进行训练，要求自行设计CNN模型的层数（主要是卷积层、池化层等）和结构（包含卷积核数量、激活函数等）； （4）合理的设计训练批量和训练次数等参数，要求模型最终在测试集上分类成功率达到90%以上并训练过程可视化函数和结果图像可视化

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首先，我们需要导入需要的Pytorch和其他库：

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

然后，我们需要加载CIFAR-10数据集并进行预处理：

transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100,
                                         shuffle=False, num_workers=2)

接着，我们可以自己设计CNN模型，并定义损失函数和优化器：

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(256 * 4 * 4, 512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = self.pool(nn.functional.relu(self.conv3(x)))
        x = x.view(-1, 256 * 4 * 4)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

net = Net()

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

接下来，我们可以开始训练我们的CNN模型：

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

train_losses = []
test_losses = []
train_acc = []
test_acc = []

for epoch in range(20):  # 训练20个epoch
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            train_losses.append(running_loss / 100)
            running_loss = 0.0

    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            images, labels = images.to(device), labels.to(device)
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        test_acc.append(correct / total)
        test_losses.append(criterion(outputs, labels).item())

    print('Epoch %d, Train Loss: %.3f, Test Loss: %.3f, Test Acc: %.3f' %
          (epoch + 1, train_losses[-1], test_losses[-1], test_acc[-1]))

最后，我们可以可视化训练过程和结果：

plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.plot(train_losses, label='Training Loss')
plt.plot(test_losses, label='Testing Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(test_acc)
plt.xlabel('Epoch')
plt.ylabel('Accuracy')

plt.show()

这样，我们就完成了Pytorch第三方学习框架的CNN模型对CIFAR-10数据集的分类任务。