4. 线性回归-python实现

 1 # 数据集生成
 2 import numpy as np
 3 import torch
 4 import matplotlib.pyplot as plt
 5 import torch.utils.data as Data
 6 from torch import nn
 7 from torch.nn import init
 8 
 9 num_inputs = 2
10 num_outputs = 1
11 num_samples = 1000
12 
13 weight_true = torch.tensor([[2], [-3.4]])
14 true_w = [2,-3.4]
15 bais_true = -4.2
16 features = torch.tensor(np.random.normal(0, 1, (num_samples, num_inputs)), dtype=torch.float)
17 labels = torch.mm(features, weight_true) + bais_true
18 #features = torch.tensor(np.random.normal(0, 1, (num_samples, num_inputs)), dtype=torch.float)
19 #labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + bais_true
20 labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)
21 
22 # 加载数据
23 #batch_size = 50
24 #dataset = Data.TensorDataset(features, labels)
25 #dataiter = Data.DataLoader(dataset, batch_size, shuffle=True)
26 
27 batch_size = 10
28 # 将训练数据的特征和标签组合
29 dataset = Data.TensorDataset(features, labels)
30 # 随机读取小批量
31 data_iter = Data.DataLoader(dataset, batch_size, shuffle=True)
32 
33 # 模型
34 class LineNet(nn.Module):
35     def __init__(self, n_feature):
36         super(LineNet, self).__init__()
37         self.linear = nn.Linear(n_feature, 1)
38     # forward 定义前向传播
39     def forward(self, x):
40         y = self.linear(x)
41         return y
42 
43 net = LineNet(num_inputs)
44 print(net)
45 
46 # 模型参数
47 init.normal_(net.linear.weight, mean=0, std=0.01)
48 init.constant_(net.linear.bias, val=0)  # 也可以直接修改bias的data: net[0].bias.data.fill_(0)
49 
50 # 损失函数
51 loss = nn.MSELoss()
52 # 算法优化
53 optimzer = torch.optim.SGD(net.parameters(),lr=0.04)
54 # 训练
55 num_epochs = 100
56 for epoch in range(num_epochs):
57     train_l = 0.0
58     for X,y in data_iter:
59         output = net(X)
60         l = loss(output, y.view(-1, 1))
61         optimzer.zero_grad() # 梯度清零，等价于net.zero_grad()
62         l.backward()
63         optimzer.step()
64 
65         train_l+=l
66     print('epoch %d ,loss %.4f' % (epoch + 1, train_l))
67     print(f'权重weight:{net.linear.weight}，偏差bais{net.linear.bias}')
68 
69 
70 
71 plt.scatter(features[:, 0].numpy(), labels.numpy(), 10, 'r')
72 plt.scatter(features[:, 1].numpy(), labels.numpy(), 10, 'g')
73 plt.show()
运行结果：
抛出问题：在做features数据集使用的是标准正态分布，但如果std != 1 就是拟合不出结果，如果那个高手知道。麻烦指导下，谢谢。