划分jaffe图片数据集的代码，划分为训练集、验证集和测试集

可以使用Python语言的scikit-learn库中的train_test_split函数进行划分，代码如下：

from sklearn.model_selection import train_test_split

# 将图片数据集和标签加载到x和y中
x = ... # 图片数据集
y = ... # 图片标签

# 将数据集划分为训练集、验证集和测试集，其中test_size参数表示测试集占总数据集的比例
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=42)

# 确认划分的结果
print("训练集数量：", len(x_train))
print("验证集数量：", len(x_val))
print("测试集数量：", len(x_test))

这段代码中，首先从scikit-learn库中导入了train_test_split函数。然后将图片数据集和标签加载到x和y中，调用train_test_split函数将数据集划分为训练集、验证集和测试集，其中test_size参数表示测试集占总数据集的比例。最后输出训练集、验证集和测试集的数量。

相关问题

写一个用pytorch,VGG16处理jaffe图片数据集的代码，在cpu上运行，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，我可以帮您完成这个任务。首先，您需要下载Jaffe数据集并安装PyTorch。然后，您可以按照以下步骤进行操作：

1.导入所需的库和模块。

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix

2.定义数据转换。

transform = transforms.Compose(
    [transforms.Grayscale(),
     transforms.Resize(224),
     transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))])

3.加载数据集。

data_path = 'jaffe'
train_set = torchvision.datasets.ImageFolder(root=data_path+'/train', transform=transform)
val_set = torchvision.datasets.ImageFolder(root=data_path+'/val', transform=transform)
test_set = torchvision.datasets.ImageFolder(root=data_path+'/test', transform=transform)

4.定义数据生成器。

train_loader = torch.utils.data.DataLoader(train_set, batch_size=8,
                                          shuffle=True, num_workers=2)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=8,
                                          shuffle=False, num_workers=2)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=8,
                                          shuffle=False, num_workers=2)

5.定义模型。

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.vgg = torchvision.models.vgg16(pretrained=True)
        self.fc1 = torch.nn.Linear(1000, 7)

    def forward(self, x):
        x = self.vgg(x)
        x = self.fc1(x)
        return x

net = Net()

6.定义损失函数和优化器。

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

7.训练模型。

n_epochs = 20
train_loss = []
val_loss = []
train_accuracy = []
val_accuracy = []

for epoch in range(n_epochs):
    running_loss = 0.0
    correct = 0
    total = 0
    
    # Train
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()

    train_acc = correct/total
    train_accuracy.append(train_acc)
    train_loss.append(running_loss/len(train_loader))
    
    # Evaluate on validation set
    val_running_loss = 0.0
    val_correct = 0
    val_total = 0
    with torch.no_grad():
        for i, data in enumerate(val_loader, 0):
            inputs, labels = data
            outputs = net(inputs)
            _, predicted = torch.max(outputs.data, 1)
            val_total += labels.size(0)
            val_correct += (predicted == labels).sum().item()
            val_loss_batch = criterion(outputs, labels)
            val_running_loss += val_loss_batch.item()
    
    val_acc = val_correct/val_total
    val_accuracy.append(val_acc)
    val_loss.append(val_running_loss/len(val_loader))

    print(f'Epoch {epoch+1}, Train Loss: {train_loss[-1]:.3f}, Val Loss: {val_loss[-1]:.3f}, Train Acc: {train_acc:.3f}, Val Acc: {val_acc:.3f}')

print('Finished Training')

8.测试模型。

test_correct = 0
test_total = 0
predicted = []
true = []

with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = net(images)
        _, pred = torch.max(outputs.data, 1)
        test_total += labels.size(0)
        test_correct += (pred == labels).sum().item()
        predicted.extend(pred.tolist())
        true.extend(labels.tolist())

print(f'Test Accuracy: {test_correct/test_total:.3f}')

confusion = confusion_matrix(true, predicted)
print(f'Confusion Matrix:\n{confusion}')

9.绘制准确率图，损失图和混淆矩阵。

plt.figure()
plt.plot(range(n_epochs), train_accuracy, label='Train')
plt.plot(range(n_epochs), val_accuracy, label='Val')
plt.title('Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

plt.figure()
plt.plot(range(n_epochs), train_loss, label='Train')
plt.plot(range(n_epochs), val_loss, label='Val')
plt.title('Cross Entropy Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

plt.figure()
plt.imshow(confusion, cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.xlabel('Predictions')
plt.ylabel('True Labels')
plt.xticks(np.arange(7), ('angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise'), rotation=45)
plt.show()

希望我的答案能够满足您的要求，有任何问题请随时与我联系。

将jaffe划分为训练集，验证集和测试集的代码

以下是将Jaffe数据集划分为训练集、验证集和测试集的Python代码：

import os
import random
import shutil

# 数据集路径
data_dir = 'jaffe'

# 划分比例
train_ratio = 0.6
val_ratio = 0.2
test_ratio = 0.2

# 获取数据集列表
data_list = os.listdir(data_dir)
random.shuffle(data_list)

# 计算划分点
num_data = len(data_list)
num_train = int(num_data * train_ratio)
num_val = int(num_data * val_ratio)
num_test = num_data - num_train - num_val

# 划分数据集
train_list = data_list[:num_train]
val_list = data_list[num_train:num_train+num_val]
test_list = data_list[num_train+num_val:]

# 创建目标文件夹
train_dir = os.path.join(data_dir, 'train')
val_dir = os.path.join(data_dir, 'val')
test_dir = os.path.join(data_dir, 'test')
os.makedirs(train_dir, exist_ok=True)
os.makedirs(val_dir, exist_ok=True)
os.makedirs(test_dir, exist_ok=True)

# 复制文件到目标文件夹
for filename in train_list:
    src_path = os.path.join(data_dir, filename)
    dst_path = os.path.join(train_dir, filename)
    shutil.copy(src_path, dst_path)
for filename in val_list:
    src_path = os.path.join(data_dir, filename)
    dst_path = os.path.join(val_dir, filename)
    shutil.copy(src_path, dst_path)
for filename in test_list:
    src_path = os.path.join(data_dir, filename)
    dst_path = os.path.join(test_dir, filename)
    shutil.copy(src_path, dst_path)

以上代码首先设置了数据集路径 `data_dir` 和划分比例 `train_ratio`、`val_ratio`、`test_ratio`。然后随机打乱数据集列表，并根据比例计算训练集、验证集和测试集的数量。接着创建目标文件夹，并将文件根据划分写入到对应的目标文件夹中。