在训练时期使用测试数据集的PyTorch教程

import torch
from torch.utils.data import Dataset

class CustomDataset(Dataset):
    def __init__(self, data):
        self.data = data
    
    def __getitem__(self, index):
        # 返回数据和标签
        return self.data[index][0], self.data[index][1]
    
    def __len__(self):
        return len(self.data)

from torch.utils.data import DataLoader

# 假设我们有一个数据集data，其中包含了训练数据和测试数据
train_data = data[:800]
test_data = data[800:]

# 创建训练集和测试集的数据加载器
train_dataset = CustomDataset(train_data)
test_dataset = CustomDataset(test_data)

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

import torch.nn as nn
import torch.optim as optim

# 定义模型
model = MyModel()

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

# 训练模型
for epoch in range(num_epochs):
    model.train()
    for inputs, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    
    # 在每个epoch结束后，使用测试数据集评估模型
    model.eval()
    total_correct = 0
    total_samples = 0
    with torch.no_grad():
        for inputs, labels in test_loader:
            outputs = model(inputs)
            _, predicted = torch.max(outputs, 1)
            total_correct += (predicted == labels).sum().item()
            total_samples += labels.size(0)
    
    accuracy = total_correct / total_samples
    print(f"Epoch {epoch+1}: Test Accuracy = {accuracy}")