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，我懂了。以下是您所要求的代码和结果。注意，这是一个基本的代码示例，可以根据您的需要进行更改。

首先，我们需要导入所需的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import matplotlib.pyplot as plt
import numpy as np
import os
from sklearn.metrics import confusion_matrix

接下来，我们需要下载和加载Jaffe数据集，然后将其划分为训练集，验证集和测试集：

# Download and load Jaffe dataset
!wget https://zenodo.org/record/3451524/files/jaffe.tar.gz
!tar xvzf jaffe.tar.gz

# Create train/val/test splits
dataset = datasets.ImageFolder('jaffe', transform=transforms.Compose([transforms.Grayscale(), transforms.Resize((224, 224)), transforms.ToTensor()]))
n = len(dataset)
split = [int(0.7*n), int(0.1*n)]
trainset, valset, testset = torch.utils.data.random_split(dataset, [split[0], split[1], n-(split[0]+split[1])])

然后，我们需要定义VGG16模型：

class VGG16(nn.Module):
    def __init__(self):
        super(VGG16, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 7),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

现在我们需要定义训练和验证函数，并在训练过程中计算损失和准确率，以及可视化训练和验证的结果：

def train(model, train_loader, criterion, optimizer):
    model.train()
    train_loss = 0.0
    train_corrects = 0
    for data, labels in train_loader:
        data = data.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(data)
        _, preds = torch.max(outputs.data, 1)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * data.size(0)
        train_corrects += torch.sum(preds == labels.data)
    train_loss = train_loss / len(train_loader.dataset)
    train_acc = train_corrects.double() / len(train_loader.dataset)
    return train_loss, train_acc

def validate(model, val_loader, criterion):
    model.eval()
    val_loss = 0.0
    val_corrects = 0
    with torch.no_grad():
        for data, labels in val_loader:
            data = data.to(device)
            labels = labels.to(device)
            outputs = model(data)
            _, preds = torch.max(outputs.data, 1)
            loss = criterion(outputs, labels)
            val_loss += loss.item() * data.size(0)
            val_corrects += torch.sum(preds == labels.data)
        val_loss = val_loss / len(val_loader.dataset)
        val_acc = val_corrects.double() / len(val_loader.dataset)
    return val_loss, val_acc

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

# Define hyperparameters
batch_size = 64
learning_rate = 0.001
num_epochs = 50

# Create data loaders
train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(valset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(testset, batch_size=batch_size, shuffle=True)

# Create VGG16 model and send to device
model = VGG16().to(device)

# Define optimizer and loss function
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()

# Train and validate model
train_losses = []
train_accs = []
val_losses = []
val_accs = []
for epoch in range(num_epochs):
    train_loss, train_acc = train(model, train_loader, criterion, optimizer)
    val_loss, val_acc = validate(model, val_loader, criterion)
    train_losses.append(train_loss)
    train_accs.append(train_acc)
    val_losses.append(val_loss)
    val_accs.append(val_acc)
    print('Epoch: {} Train Loss: {:.4f} Train Acc: {:.4f} Val Loss: {:.4f} Val Acc: {:.4f}'.format(epoch+1, train_loss, train_acc, val_loss, val_acc))

# Test model on test set
test_corrects = 0
test_predictions = []
test_labels = []
for data, labels in test_loader:
    data = data.to(device)
    labels = labels.to(device)
    outputs = model(data)
    _, preds = torch.max(outputs.data, 1)
    test_corrects += torch.sum(preds == labels.data)
    test_predictions += preds.tolist()
    test_labels += labels.tolist()
test_acc = test_corrects.double() / len(test_loader.dataset)

# Plot results
plt.figure()
plt.plot(range(num_epochs), train_losses, label='train')
plt.plot(range(num_epochs), val_losses, label='val')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

plt.figure()
plt.plot(range(num_epochs), train_accs, label='train')
plt.plot(range(num_epochs), val_accs, label='val')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

conf_matrix = confusion_matrix(test_labels, test_predictions)
plt.figure()
plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.colorbar()
tick_marks = np.arange(len(dataset.classes))
plt.xticks(tick_marks, dataset.classes, rotation=45)
plt.yticks(tick_marks, dataset.classes)
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
for i in range(len(dataset.classes)):
    for j in range(len(dataset.classes)):
        plt.text(j, i, conf_matrix[i, j], horizontalalignment="center", color="white" if conf_matrix[i, j] > conf_matrix.max() / 2 else "black")
plt.show()

print('Test Accuracy: {:.4f}'.format(test_acc))

这将输出您所需的结果，包括损失图，准确率图和混淆矩阵，以及测试集上的精度。