深度学习训练集验证集代码框架

深度学习模型的训练集和验证集的代码框架一般如下：

import torch
from torch.utils.data import DataLoader, Dataset
from sklearn.model_selection import train_test_split

# 定义数据集类
class MyDataset(Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels

    def __getitem__(self, index):
        return self.data[index], self.labels[index]

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

# 加载数据
data = ...
labels = ...
train_data, val_data, train_labels, val_labels = train_test_split(data, labels, test_size=0.2)

# 定义数据加载器
train_dataset = MyDataset(train_data, train_labels)
val_dataset = MyDataset(val_data, val_labels)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32)

# 定义模型和优化器
model = ...
optimizer = ...

# 训练模型
for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        # 前向传播
        outputs = model(inputs)
        loss = ...

        # 反向传播
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # 在验证集上测试模型
    with torch.no_grad():
        total_correct = 0
        total_samples = 0
        for inputs, labels in val_loader:
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total_samples += labels.size(0)
            total_correct += (predicted == labels).sum().item()

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

其中，`MyDataset` 类用于定义数据集，`train_test_split` 函数用于将数据集划分为训练集和验证集，`DataLoader` 类用于定义数据加载器，`model` 和 `optimizer` 分别表示模型和优化器。在训练过程中，我们先在训练集上进行训练，然后在验证集上测试模型的性能。