Pytorch实现线性回归
import torchx_data = torch.Tensor([,,])
y_data = torch.Tensor([,,])
class MyLinear(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(1,1)
def forward(self, x):
y_pred = self.linear(x)
return y_pred
model = MyLinear()
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
for epoch in range(1000):
y_pred = model(x_data)
loss = criterion(y_pred,y_data)
print('epoch==' + str(epoch), 'loss==' + str(loss.item()))
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('w=',model.linear.weight.item())
print('b=',model.linear.bias.item())
x_test = torch.tensor([])
y_test = model(x_test)
print("y_test=",y_test.data)输出结果：
https://img-blog.csdnimg.cn/8156030505074afea3aac9c533b30818.png
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