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，我来回答你的问题。以下是用pytorch和VGG16处理jaffe数据集的代码，给出了准确率图，损失图和混淆矩阵。

首先，导入所需的库：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision
from torchvision import datasets, models, transforms
import numpy as np
import time
import os
import copy
from sklearn.metrics import confusion_matrix

然后，定义一些参数：

data_dir = "/path/to/jaffe/dataset"
batch_size = 4
num_workers = 4
num_epochs = 25
input_size = 224
learning_rate = 0.001

接着，定义数据预处理：

data_transforms = {
    'train': transforms.Compose([
        transforms.Resize(input_size),
        transforms.RandomHorizontalFlip(),
        transforms.RandomRotation(10),
        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
    ]),
    'val': transforms.Compose([
        transforms.Resize(input_size),
        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
    ]),
    'test': transforms.Compose([
        transforms.Resize(input_size),
        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
    ])
}

然后，加载数据集：

image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val', 'test']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True, num_workers=num_workers) for x in ['train', 'val', 'test']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}
class_names = image_datasets['train'].classes

接下来，定义VGG16模型：

model_ft = models.vgg16(pretrained=True)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, len(class_names))

然后，定义损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=learning_rate, momentum=0.9)

接着，定义学习率调度器：（可选）

exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

然后，定义训练和测试函数：

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0
    train_losses = []
    val_losses = []
    train_acc = []
    val_acc = []
    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                optimizer.zero_grad()

                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            if phase == 'train':
                scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            if phase == 'train':
                train_losses.append(epoch_loss)
                train_acc.append(epoch_acc)
            else:
                val_losses.append(epoch_loss)
                val_acc.append(epoch_acc)

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:.4f}'.format(best_acc))

    model.load_state_dict(best_model_wts)
    return model, train_losses, val_losses, train_acc, val_acc

def test_model(model):
    model.eval()
    test_losses = []
    test_acc = []
    y_true = []
    y_pred = []
    with torch.no_grad():
        for inputs, labels in dataloaders['test']:
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)
            loss = criterion(outputs, labels)

            y_true += labels.cpu().numpy().tolist()
            y_pred += preds.cpu().numpy().tolist()

            test_losses.append(loss.item() * inputs.size(0))
            test_acc.append(torch.sum(preds == labels.data))

        epoch_loss = sum(test_losses) / dataset_sizes['test']
        epoch_acc = sum(test_acc).double() / dataset_sizes['test']
        conf_mat = confusion_matrix(y_true, y_pred)

        print('Test Loss: {:.4f} Acc: {:.4f}'.format(epoch_loss, epoch_acc))
        print('Confusion Matrix:')
        print(conf_mat)

    return epoch_loss, epoch_acc, conf_mat

最后，运行代码，得到准确率图，损失图和混淆矩阵：

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

model_ft = model_ft.to(device)
criterion = criterion.to(device)

model_ft, train_losses, val_losses, train_acc, val_acc = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=num_epochs)
test_loss, test_acc, conf_mat = test_model(model_ft)

其中，train_losses、val_losses、train_acc和val_acc分别为训练集和验证集上的损失和准确率数组，test_loss、test_acc和conf_mat为测试集上的损失、准确率和混淆矩阵。

我们可以使用matplotlib绘制准确率图和损失图：

import matplotlib.pyplot as plt

plt.plot(train_losses, label='Training loss')
plt.plot(val_losses, label='Validation loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

plt.plot(train_acc, label='Training accuracy')
plt.plot(val_acc, label='Validation accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

混淆矩阵可以使用seaborn绘制：

import seaborn as sns

sns.heatmap(conf_mat, annot=True, cmap='Blues', xticklabels=class_names, yticklabels=class_names)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()

这样就完成了用pytorch和VGG16处理jaffe数据集的代码，并且给出了准确率图，损失图和混淆矩阵。