九大Pytorch最重要操作！！

import torch

# 创建张量
a = torch.tensor([1, 2, 3])
b = torch.tensor([4, 5, 6])

# 张量加法
c = a + b
print(c)

x = torch.tensor([1.0], requires_grad=True)
y = x**2
y.backward()
print(x.grad)

import torch.nn as nn

class SimpleNN(nn.Module):
      def __init__(self):
         super(SimpleNN, self).__init__()
         self.fc = nn.Linear(10, 5)

      def forward(self, x):
         return self.fc(x)

model = SimpleNN()

import torch.optim as optim

optimizer = optim.SGD(model.parameters(), lr=0.01)

loss_function = nn.CrossEntropyLoss()

from torch.utils.data import DataLoader, Dataset

class CustomDataset(Dataset):
      # 实现数据集的初始化和__getitem__方法

dataloader = DataLoader(dataset, batch_size=64, shuffle=True)

# 保存模型
torch.save(model.state_dict(), 'model.pth')

# 加载模型
loaded_model = SimpleNN()
loaded_model.load_state_dict(torch.load('model.pth'))

from torch.optim import lr_scheduler

scheduler = lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)

model.eval()
with torch.no_grad():
      # 运行模型并计算性能指标