写一个用resnet18分析jaffe数据集的代码，给出损失图，准确率图和混淆矩阵

好的，我会尽力回答你的问题，以下是代码示例：

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
from sklearn.metrics import confusion_matrix

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

# 数据预处理
transform = transforms.Compose([transforms.Resize((224,224)),
                                transforms.ToTensor(),
                                transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))])

train_dataset = torchvision.datasets.ImageFolder(root='jaffe/train', transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=5, shuffle=True, num_workers=2)

test_dataset = torchvision.datasets.ImageFolder(root='jaffe/test', transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=5, shuffle=False, num_workers=2)

classes = train_dataset.classes

# 定义模型
class ResNet18(nn.Module):
    def __init__(self, num_classes=7):
        super(ResNet18, self).__init__()
        self.resnet18 = torchvision.models.resnet18(pretrained=True)
        self.linear = nn.Linear(1000, num_classes)

    def forward(self, x):
        x = self.resnet18(x)
        x = self.linear(x)
        return x

model = ResNet18()
model.to(device)

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

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i,data in enumerate(train_loader,0):
        inputs,labels = data
        inputs,labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()

        optimizer.step()

        running_loss += loss.item()
        if i % 10 == 9:
            print('[%d,%5d] loss: %.3f' % (epoch+1,i+1,running_loss/10))
            running_loss = 0.0

print('Finished Training')

# 测试模型
correct = 0
total = 0
predictions = []
true_labels = []
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        
        _, predicted = torch.max(outputs.data, 1)
        predictions += predicted.tolist()
        true_labels += labels.tolist()
        
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

# 混淆矩阵
confusion_mat = confusion_matrix(true_labels, predictions)
print(confusion_mat)

# 准确率图
plt.plot([0,1,2,3,4,5,6], confusion_mat.diagonal()/confusion_mat.sum(1))
plt.xlabel('Classes')
plt.ylabel('Accuracy')
plt.show()

# 损失图
plt.plot(loss_list)
plt.xlabel('Iteration')
plt.ylabel('Loss')
plt.show()

请注意，上述示例仅作参考，需要根据具体情况进行修改。同时，混淆矩阵、准确率图和损失图的绘制需要额外的代码实现，不在此处展示。