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

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以下是使用PyTorch和VGG16处理jaffe数据集的代码，帮助您划分数据集、训练网络、验证准确率和测试网络。

首先，安装必要的库：

!pip install torch torchvision
!pip install scikit-learn pandas matplotlib

导入库：

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

定义一些超参数：

batch_size = 32
num_epochs = 50
learning_rate = 0.001

准备数据集：

class JaffeDataset(Dataset):
    def __init__(self, csv_file, root_dir, transform=None):
        self.landmarks_frame = pd.read_csv(csv_file)
        self.root_dir = root_dir
        self.transform = transform

    def __len__(self):
        return len(self.landmarks_frame)

    def __getitem__(self, idx):
        img_name = os.path.join(self.root_dir, self.landmarks_frame.iloc[idx, 0])
        image = io.imread(img_name)
        label = self.landmarks_frame.iloc[idx, 1]
        if self.transform:
            image = self.transform(image)
        return image, label

data_transforms = {
    'train': transforms.Compose([transforms.ToPILImage(),
                                 transforms.Resize((224, 224)),
                                 transforms.RandomHorizontalFlip(),
                                 transforms.ToTensor(),
                                 transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
                                ]),
    'val': transforms.Compose([transforms.ToPILImage(),
                               transforms.Resize((224, 224)),
                               transforms.ToTensor(),
                               transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
                              ])
}

train_dataset = JaffeDataset(csv_file='jaffe_training.csv', root_dir='jaffe/', transform=data_transforms['train'])
val_dataset = JaffeDataset(csv_file='jaffe_validation.csv', root_dir='jaffe/', transform=data_transforms['val'])
test_dataset = JaffeDataset(csv_file='jaffe_testing.csv', root_dir='jaffe/', transform=data_transforms['val'])

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)

定义模型，这里使用预训练的VGG16网络，并且只修改最后一个全连接层的输出维度：

class VGG16(nn.Module):
    def __init__(self, num_classes=7):
        super(VGG16, self).__init__()
        self.features = models.vgg16(pretrained=True).features
        self.avgpool = nn.AdaptiveAvgPool2d(output_size=(7, 7))
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

model = VGG16(num_classes=7)

配置GPU或CPU运行环境：

device = torch.device("cpu")
if torch.cuda.is_available():
    device = torch.device("cuda:0")
    if torch.cuda.device_count() > 1:
        print("Using", torch.cuda.device_count(), "GPUs!")
        model = nn.DataParallel(model)
    model.to(device)

定义损失函数和优化器：

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

训练模型：

train_loss = []
val_loss = []
train_acc = []
val_acc = []

for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    running_corrects = 0
    for i, (inputs, labels) in enumerate(train_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * inputs.size(0)
        _, preds = torch.max(outputs, 1)
        running_corrects += torch.sum(preds == labels.data)
    train_loss.append(running_loss / len(train_dataset))
    train_acc.append(running_corrects.double() / len(train_dataset))

    model.eval()
    with torch.no_grad():
        running_loss = 0.0
        running_corrects = 0
        for i, (inputs, labels) in enumerate(val_loader):
            inputs = inputs.to(device)
            labels = labels.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            running_loss += loss.item() * inputs.size(0)
            _, preds = torch.max(outputs, 1)
            running_corrects += torch.sum(preds == labels.data)
        val_loss.append(running_loss / len(val_dataset))
        val_acc.append(running_corrects.double() / len(val_dataset))

    print("[Epoch %d] Train loss: %.3f Train acc: %.3f Val loss: %.3f Val acc: %.3f" % 
          (epoch+1, train_loss[-1], train_acc[-1], val_loss[-1], val_acc[-1]))

plt.plot(train_loss, label='train')
plt.plot(val_loss, label='validation')
plt.legend()
plt.title('Loss')
plt.show()

plt.plot(train_acc, label='train')
plt.plot(val_acc, label='validation')
plt.legend()
plt.title('Accuracy')
plt.show()

计算测试准确率和混淆矩阵：

model.eval()
y_pred = []
y_true = []
with torch.no_grad():
    for i, (inputs, labels) in enumerate(test_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)
        outputs = model(inputs)
        _, preds = torch.max(outputs, 1)
        y_pred.extend(list(preds.cpu().numpy()))
        y_true.extend(list(labels.cpu().numpy()))

cm = confusion_matrix(y_true, y_pred)
print(cm)

最后，您将得到训练和验证准确率、损失函数图、测试准确率和混淆矩阵。请注意，这些结果将因训练时的初始化和超参数设置而略有不同。

希望这个代码示例可以帮助您！