写一个用pytorch，VGG16分析JAFFE数据的代码，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，我理解您的要求了。对于您的问题，我建议您参考以下的代码：

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

# 定义超参数
num_epochs = 10
num_classes = 7
batch_size = 16
learning_rate = 0.001

# 加载数据集
transform = transforms.Compose(
    [transforms.Resize((224, 224)),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
                          std=[0.229, 0.224, 0.225])])

train_dataset = torchvision.datasets.ImageFolder(root='./train/',
                                                 transform=transform)

test_dataset = torchvision.datasets.ImageFolder(root='./test/',
                                                transform=transform)

# 划分数据集
train_size = int(0.8 * len(train_dataset))
val_size = len(train_dataset) - train_size

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

train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size, shuffle=True)

val_loader = torch.utils.data.DataLoader(dataset=val_dataset,
                                         batch_size=batch_size, shuffle=False)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size, shuffle=False)

# 定义 VGG16 模型
vgg = torchvision.models.vgg16(pretrained=True)

for param in vgg.parameters():
    param.requires_grad = False

vgg.classifier[6] = nn.Sequential(
                      nn.Linear(4096, 256), 
                      nn.ReLU(), 
                      nn.Dropout(0.4),
                      nn.Linear(256, num_classes),                   
                      nn.LogSoftmax(dim=1))

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(vgg.classifier[6].parameters(), lr=learning_rate)

# 训练模型并保存最优模型
total_step = len(train_loader)
val_loss_min = np.Inf

for epoch in range(num_epochs):
    running_loss = 0.0
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)

        # 前向传播
        outputs = vgg(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

        if (i + 1) % 10 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))

    # 每轮训练后在验证集上计算准确率和损失
    val_loss = 0
    accuracy = 0

    with torch.no_grad():
        for images, labels in val_loader:
            images = images.to(device)
            labels = labels.to(device)

            outputs = vgg(images)
            val_loss += criterion(outputs, labels).item()

            _, predicted = torch.max(outputs.data, 1)
            accuracy += (predicted == labels).sum().item()

    val_loss /= len(val_loader)
    accuracy /= len(val_loader)

    print('Epoch [{}/{}], Loss: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.2f}%'.format(epoch + 1, num_epochs, running_loss / total_step, val_loss, accuracy * 100))

    # 保存最优模型
    if val_loss <= val_loss_min:
        print("Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...".format(val_loss_min, val_loss))
        torch.save(vgg.state_dict(), 'best_model.pt')
        val_loss_min = val_loss

# 加载最优模型并在测试集上计算准确率和混淆矩阵
vgg.load_state_dict(torch.load('best_model.pt'))

with torch.no_grad():
    correct = 0
    total = 0
    y_true = []
    y_pred = []
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = vgg(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
        y_true += labels.tolist()
        y_pred += predicted.tolist()

    print('Test Accuracy of the model on the {} test images: {:.2f}%'.format(total, (correct / total) * 100))

    from sklearn.metrics import confusion_matrix
    print(confusion_matrix(y_true, y_pred))

在这个代码中，我们使用 VGG16 来对 JAFFE 数据集进行分类，并且将数据集划分成了训练集、验证集和测试集，然后用 pytorch 搭建了 VGG16 模型，并对其进行训练，最后计算了在测试集上的准确率和混淆矩阵。您可以根据自己的需要来修改这个代码，并添加您所需要的准确率图和损失图等。