我在youtube上关注一系列关于深度学习的教程，我遇到了一个令我困惑的问题.
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_predicted):
return ((y-y_predicted)**2).mean()
print(f'Prediction before training: f(5) = {forward(5):.3f}')
learning_rate= 0.01
epoch = 20
for i in range(epoch):
y_pred = forward(X)
l = loss(Y, y_pred)
l.backward()
with torch.no_grad():
w = w - learning_rate * w.grad
# (w -= learning_rate * w.grad) # would not cause error in the following line
w.grad.zero_() #error : 'NoneType' object has no attribute 'zero_'
if i % 1 ==0:
print(f'weight : {w}, loss : {l}')
我真的很想知道"w = w - learning_rate * w.grad"和"w -= learning_rate * w.grad"之间的区别，因为这两个在我的经验中是一样的.谢谢