了解PyTorch,虽然啥也看不懂，但是这个东西也许有用
1: PyTorch基础
import torch
import torch.nn as nn
import torch.optim as optim# 1.1 张量基础
print("PyTorch版本:", torch.__version__)
print("CUDA是否可用:", torch.cuda.is_available())# 创建张量
x = torch.tensor([1.0, 2.0, 3.0])
y = torch.tensor([4.0, 5.0, 6.0])# 张量运算
z = x + y
print(f"张量加法: {z}")# 自动微分
x = torch.tensor(2.0, requires_grad=True)
y = x**2 + 3*x + 1
y.backward()
print(f"dy/dx at x=2: {x.grad}")# 1.2 简单的神经网络
class SimpleNN(nn.Module):def __init__(self):super(SimpleNN, self).__init__()self.layer1 = nn.Linear(10, 5) # 10个输入，5个输出self.layer2 = nn.Linear(5, 2) # 5个输入，2个输出self.relu = nn.ReLU()def forward(self, x):x = self.relu(self.layer1(x))x = self.layer2(x)return x# 创建模型实例
model = SimpleNN()
print("模型结构:")
print(model)# 1.3 数据加载
from torch.utils.data import Dataset, DataLoader
import numpy as npclass CustomDataset(Dataset):def __init__(self, data, labels):self.data = torch.FloatTensor(data)self.labels = torch.LongTensor(labels)def __len__(self):return len(self.data)def __getitem__(self, idx):return self.data[idx], self.labels[idx]# 创建模拟数据
data = np.random.randn(1000, 10) # 1000个样本，每个10个特征
labels = np.random.randint(0, 2, 1000) # 二分类标签dataset = CustomDataset(data, labels)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)# 1.4 训练循环
def train_one_epoch(model, dataloader, criterion, optimizer):model.train()total_loss = 0for batch_data, batch_labels in dataloader:# 前向传播outputs = model(batch_data)loss = criterion(outputs, batch_labels)# 反向传播optimizer.zero_grad()loss.backward()optimizer.step()total_loss += loss.item()return total_loss / len(dataloader)# 训练配置
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)# 训练模型
for epoch in range(5):avg_loss = train_one_epoch(model, dataloader, criterion, optimizer)print(f"Epoch {epoch+1}, Loss: {avg_loss:.4f}")
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