利用Python编写的StopAfterNEvalsHook函数：生成20条随机标题示例

下面是一个利用Python编写的StopAfterNEvalsHook函数的示例代码：

import torch
import torchvision
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision import transforms
from torch.utils.data import random_split

# 定义自定义的数据集类
class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, samples):
        self.samples = samples

    def __getitem__(self, idx):
        return self.samples[idx]

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

# 自定义停止训练的Hook
class StopAfterNEvalsHook:
    def __init__(self, n_evals):
        self.n_evals = n_evals
        self.eval_count = 0

    def __call__(self, trainer):
        self.eval_count += 1
        if self.eval_count >= self.n_evals:
            trainer.should_stop = True

# 创建随机标题数据集
random_titles = [
    "How to improve your memory",
    "The benefits of regular exercise",
    "Efficient ways to lose weight",
    "Tips for stress management",
    "10 healthy breakfast ideas",
    "The importance of a balanced diet",
    "Ways to boost your productivity",
    "The impact of social media on mental health",
    "How to overcome procrastination",
    "Why a good night's sleep is essential",
    "The benefits of meditation",
    "Tips for effective time management",
    "Healthy habits for a happier life",
    "Ways to improve your communication skills",
    "The power of positive thinking",
    "Effective strategies for goal setting",
    "The importance of self-care",
    "How to develop a growth mindset",
    "Tips for better concentration",
    "The role of gratitude in happiness"
]

# 创建自定义数据集实例
dataset = CustomDataset(random_titles)

# 将数据集划分为训练集和验证集
train_size = int(0.8 * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])

# 创建DataLoader加载器
train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False)

# 定义模型架构
class Model(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Model, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 创建模型实例
input_size = len(max(random_titles, key=len))
hidden_size = 16
output_size = 2
model = Model(input_size, hidden_size, output_size)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 定义训练器
class Trainer:
    def __init__(self,
                 model,
                 criterion,
                 optimizer,
                 train_loader,
                 val_loader,
                 stop_hook=None,
                 num_epochs=10):
        self.model = model
        self.criterion = criterion
        self.optimizer = optimizer
        self.train_loader = train_loader
        self.val_loader = val_loader
        self.stop_hook = stop_hook
        self.num_epochs = num_epochs
        self.should_stop = False

    def train(self):
        for epoch in range(self.num_epochs):
            if self.should_stop:
                break
            
            # 训练模型
            self.model.train()
            for inputs in self.train_loader:
                self.optimizer.zero_grad()
                outputs = self.model(inputs)
                loss = self.criterion(outputs, labels)
                loss.backward()
                self.optimizer.step()
                
            # 模型评估
            if epoch % 5 == 0:
                self.model.eval()
                for inputs in self.val_loader:
                    outputs = self.model(inputs)
                    _, predicted = torch.max(outputs.data, 1)
                    accuracy = (predicted == labels).sum().item() / labels.size(0)

                    print('Epoch [{}/{}], Loss: {:.4f}, Accuracy: {:.2f}%'
                          .format(epoch + 1, self.num_epochs, loss.item(), accuracy * 100))

                    if self.stop_hook is not None:
                        self.stop_hook(self)

# 创建训练器实例并开始训练
stop_hook = StopAfterNEvalsHook(n_evals=20)
trainer = Trainer(model, criterion, optimizer, train_loader, val_loader, stop_hook=stop_hook, num_epochs=100)
trainer.train()

以上示例代码演示了如何利用自定义的StopAfterNEvalsHook函数来控制训练的停止。在训练过程中，每经过n_evals次评估，训练器将停止训练。在示例中，设置n_evals为20，即代表训练器将在执行20次评估后停止训练。

请注意，以上示例代码并未完整实现数据集加载、输入处理和标签定义等功能。你可以根据实际需求进行适当修改和补充。