参考： https://github.com/Iallen520/lhy_DL_Hw/blob/master/PyTorch_Introduction.ipynb
模拟一个回归模型，y = X * w + 随机数
如，y : n*1矩阵， X : n*2矩阵 , w : 1,2矩阵
设置true_w = [[-1.0], [2.0]] , 随机初始化w 比如[[1.0], [0.0]]，目标是拟合出正确的w
代码如下：
#自己写一个test case import torch d = 2 n = 50 X = torch.randn(n,d) true_w = torch.tensor([[-1.0], [2.0]]) y = X @ true_w + torch.randn(n,1) * 0.1 print('X shape', X.shape) print('y shape', y.shape) print('w shape', true_w.shape) print(X.shape) print(true_w.shape) #w = torch.rand(2,1, requires_grad = True) w = torch.tensor([[1.],[0]], requires_grad= True) print("w:", w) print('iter, loss, w') for i in range(20): loss = torch.norm(y - torch.matmul(X,w))**2 / n loss.backward() w.data = w.data - 0.1 * w.grad print('{}, {:.2f}, {}'.format(i, loss.item(), w.view(2).detach().numpy())) w.grad.zero_() print(' true w ', true_w.view(2).numpy()) print('estimated w ', w.view(2).detach().numpy())
X shape torch.Size([50, 2])
y shape torch.Size([50, 1])
w shape torch.Size([2, 1])
torch.Size([50, 2])
torch.Size([2, 1])
w: tensor([[1.],
[0.]], requires_grad=True)
iter, loss, w
0, 6.20, [0.7062541 0.32114884]
1, 4.45, [0.45446268 0.59016734]
2, 3.20, [0.2387764 0.81564814]
3, 2.30, [0.05413117 1.0047431 ]
4, 1.65, [-0.10385066 1.1634098 ]
5, 1.19, [-0.23894812 1.2966142 ]
6, 0.86, [-0.3544196 1.4084985]
7, 0.62, [-0.4530713 1.5025208]
8, 0.45, [-0.5373176 1.5815694]
9, 0.32, [-0.60923356 1.6480589 ]
10, 0.24, [-0.6706013 1.7040086]
11, 0.17, [-0.7229501 1.7511086]
12, 0.13, [-0.76759106 1.7907746 ]
13, 0.09, [-0.8056477 1.8241924]
14, 0.07, [-0.8380821 1.8523566]
15, 0.05, [-0.86571765 1.8761011 ]
16, 0.04, [-0.8892586 1.8961263]
17, 0.03, [-0.90930706 1.91302 ]
18, 0.02, [-0.9263775 1.9272763]
19, 0.02, [-0.9409093 1.9393103]
true w [-1. 2.]
estimated w [-0.9409093 1.9393103]
出错点：
1.初始化tensor时，要为float，否则容易报错
2. 初始化时设置 requires_grad = True
3. 定义的loss要在for循坏之内
4. 要用w.data不用w，否则报错，可能和pytorch初始化有关
5. w.grad.zero_() 注意这个写法