学习 PyTorch 的方法很简单

import torch# Create a 2x3 tensortensor = torch.tensor([[1, 2, 3], [4, 5, 6]])print(tensor)

# Define two tensorsa = torch.tensor([2.], requires_grad=True)b = torch.tensor([3.], requires_grad=True)# Compute resultc = a * bc.backward()# Gradientsprint(a.grad)# Gradient w.r.t a

device = "cuda" if torch.cuda.is_available() else "cpu"tensor = tensor.to(device)

x = torch.tensor([2.], requires_grad=True)y = x**2y.backward()print(x.grad)# Gradient of y w.r.t x

import torch.nn as nnclass SimpleNN(nn.Module):def __init__(self):super().__init__()self.fc = nn.Linear(1, 1)def forward(self, x):return self.fc(x)

loss_fn = nn.CrossEntropyLoss()optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

from torch.utils.data import Dataset, DataLoaderclass CustomDataset(Dataset):# ... (methods to define)data_loader = DataLoader(dataset, batch_size=32, shuffle=True)

for epoch in range(epochs):for data, target in data_loader:optimizer.zero_grad()output = model(data)loss = loss_fn(output, target)loss.backward()optimizer.step()

# Savetorch.save(model.state_dict(), 'model_weights.pth')# Loadmodel.load_state_dict(torch.load('model_weights.pth'))

scripted_model = torch.jit.script(model)scripted_model.save("model_jit.pt")