写一个用pytorch，VGG16处理jaffe数据集的代码，用cpu，划分训练集，验证集和测试集，给出准确率图，损失图和混淆矩阵

好的，我可以为您提供一些关于处理Jaffe数据集的代码。您可以使用以下代码块：

import torch
import torch.nn as nn
import torchvision.models as models
import pandas as pd
from torchvision import transforms, datasets
from torch.utils.data import DataLoader, Dataset
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt

# 设置随机数种子，确保每次运行结果一致
torch.manual_seed(0)

# 设置文件路径
file_path = '/path/to/your/jaffe/dataset'

# 创建数据转换器
transformer = transforms.Compose([
    transforms.Resize(224),  # 改变图像大小到 224 x 224
    transforms.Grayscale(num_output_channels=3),  # 转换为灰度图像
    transforms.ToTensor(),  # 转换为张量
    transforms.Normalize((0.5,), (0.5,))  # 标准化图像数据
])

# 加载数据集
dataset = datasets.ImageFolder(root=file_path, transform=transformer)

# 划分数据集为训练集，验证集和测试集
train_size = int(0.7 * len(dataset))
val_size = int(0.15 * len(dataset))
test_size = len(dataset) - train_size - val_size

train_dataset, val_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, val_size, test_size])

# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=True)

# 创建VGG16模型
model = models.vgg16(pretrained=True)

# 冻结所有层
for param in model.parameters():
    param.requires_grad = False

# 替换最后一层
model.classifier[6] = nn.Linear(4096, dataset.num_classes)

# 设置损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.classifier[6].parameters(), lr=0.001, momentum=0.9)

# 训练模型
train_losses, val_losses, val_accuracies = [], [], []
n_epochs = 10
for epoch in range(1, n_epochs+1):
    # 训练模式
    model.train()
    train_loss = 0.0
    for data, target in train_loader:
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()*data.size(0)
    train_loss = train_loss/len(train_loader.dataset)
    train_losses.append(train_loss)
    
    # 验证模式
    model.eval()
    val_loss = 0.0
    val_accuracy = 0.0
    for data, target in val_loader:
        output = model(data)
        loss = criterion(output, target)
        val_loss += loss.item()*data.size(0)
        _, pred = torch.max(output, dim=1)
        correct_tensor = pred.eq(target.data.view_as(pred))
        accuracy = torch.mean(correct_tensor.type(torch.FloatTensor))
        val_accuracy += accuracy.item()*data.size(0)
    val_loss = val_loss/len(val_loader.dataset)
    val_losses.append(val_loss)
    val_accuracy = val_accuracy/len(val_loader.dataset)
    val_accuracies.append(val_accuracy)
    print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f} \tValidation Accuracy: {:.6f}'.format(
        epoch, train_loss, val_loss, val_accuracy))

# 绘制损失和准确率图
plt.plot(train_losses, label='Training loss')
plt.plot(val_losses, label='Validation loss')
plt.plot(val_accuracies, label='Validation accuracy')
plt.legend(frameon=False)

# 测试模型
model.eval()
test_accuracy = 0.0
confusion_mat = torch.zeros(dataset.num_classes, dataset.num_classes)
with torch.no_grad():
    for data, target in test_loader:
        output = model(data)
        _, pred = torch.max(output, dim=1)
        confusion_mat += confusion_matrix(target, pred, labels=[0, 1, 2, 3, 4, 5])
        correct_tensor = pred.eq(target.data.view_as(pred))
        accuracy = torch.mean(correct_tensor.type(torch.FloatTensor))
        test_accuracy += accuracy.item()*data.size(0)

test_accuracy = test_accuracy/len(test_loader.dataset)
print('Test Accuracy: {:.6f}'.format(test_accuracy))
print('Confusion Matrix:\n', confusion_mat)

请记得将代码中的文件路径替换为您自己Jaffe数据集的路径。这段代码将训练一个使用VGG16模型的分类器，用于识别Jaffe数据集中的表情。代码会将数据集划分为训练集，验证集和测试集，并绘制损失和准确率图。同时，Training Loss: {:.6f} Validation Loss: {:.6f} Validation Accuracy: {:.6f} 将打印到控制台。最后，代码将输出测试集的准确率和混淆矩阵。