帮我生成一段用pycharm运行的用InceptionV3搭建的简单代码，不需要自己操作，不用keras，只用pytorch库。可以得到训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线

好的，以下是一个简单的使用PyTorch和InceptionV3进行图像分类的示例代码，其中包括训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线的绘制：

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

# 定义数据预处理，将图像缩放到224x224大小，并将像素值标准化到[-1, 1]
transform = transforms.Compose([
    transforms.Resize(224),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载CIFAR-10数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, 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=32, shuffle=False, num_workers=2)

# 定义InceptionV3模型
class InceptionV3(nn.Module):
    def __init__(self):
        super(InceptionV3, self).__init__()
        self.inception = torchvision.models.inception_v3(pretrained=False, aux_logits=False)
        self.fc = nn.Linear(2048, 10)

    def forward(self, x):
        x = self.inception(x)
        x = self.fc(x)
        return x

# 初始化模型、损失函数和优化器
net = InceptionV3()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练模型
train_loss_list = []
train_acc_list = []
test_loss_list = []
test_acc_list = []
num_epochs = 10
for epoch in range(num_epochs):
    train_loss = 0.0
    train_total = 0
    train_correct = 0
    test_loss = 0.0
    test_total = 0
    test_correct = 0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item() * inputs.size(0)
        _, predicted = torch.max(outputs.data, 1)
        train_total += labels.size(0)
        train_correct += (predicted == labels).sum().item()

    for data in testloader:
        inputs, labels = data
        outputs = net(inputs)
        loss = criterion(outputs, labels)

        test_loss += loss.item() * inputs.size(0)
        _, predicted = torch.max(outputs.data, 1)
        test_total += labels.size(0)
        test_correct += (predicted == labels).sum().item()

    # 计算训练集和验证集的准确率和损失函数
    train_loss_list.append(train_loss / len(trainset))
    train_acc_list.append(train_correct / train_total)
    test_loss_list.append(test_loss / len(testset))
    test_acc_list.append(test_correct / test_total)
    print('Epoch %d, Train Loss: %.3f, Train Acc: %.3f, Test Loss: %.3f, Test Acc: %.3f' %
          (epoch + 1, train_loss_list[-1], train_acc_list[-1], test_loss_list[-1], test_acc_list[-1]))

# 绘制准确率和损失函数曲线
fig, axs = plt.subplots(2, 1, figsize=(5, 5))
axs[0].plot(train_acc_list, label='Train')
axs[0].plot(test_acc_list, label='Test')
axs[0].set_title('Accuracy')
axs[0].legend()
axs[1].plot(train_loss_list, label='Train')
axs[1].plot(test_loss_list, label='Test')
axs[1].set_title('Loss')
axs[1].legend()
plt.tight_layout()
plt.show()

希望这个示例代码能够帮到你。