两个火炬张量的Select和MSELoss

import torch

tensor = torch.randn(3, 4)
indices = [0, 1, 5]  # 5超出了范围

# 检查索引是否超出范围
if all(i < tensor.size(dim) for i, dim in zip(indices, range(tensor.dim()))):
    selected_tensor = tensor.index_select(0, torch.tensor(indices))
else:
    print("索引超出范围")

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

# 假设我们有一个简单的模型和数据
model = nn.Linear(10, 1)
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 数据标准化
data = (data - data.mean()) / data.std()

# 训练过程
for epoch in range(num_epochs):
    optimizer.zero_grad()
    outputs = model(data)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()