考虑以下使用 PyTorch 实现的线性回归代码:
import torch
X = torch.tensor([1, 2, 3, 4], dtype=torch.float32)
Y = torch.tensor([2, 4, 6, 8], dtype=torch.float32)
w = torch.tensor(0.0, dtype=torch.float32, requires_grad=True)
def forward(x):
return w * x
def loss(y, y_pred):
return ((y_pred - y)**2).mean()
print(f'Prediction before training: f(5) = {forward(5).item():.3f}')
learning_rate = 0.01
n_iters = 100
for epoch in range(n_iters):
# predict = forward pass
y_pred = forward(X)
# loss
l = loss(Y, y_pred)
# calculate gradients = backward pass
l.backward()
# update weights
#w.data = …
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