Task3.PyTorch实现Logistic regression

1.PyTorch基础实现代码

 1 import torch
 2 from torch.autograd import Variable
 3 
 4 torch.manual_seed(2)
 5 x_data = Variable(torch.Tensor([[1.0], [2.0], [3.0], [4.0]]))
 6 y_data = Variable(torch.Tensor([[0.0], [0.0], [1.0], [1.0]]))
 7 
 8 #初始化
 9 w = Variable(torch.Tensor([-1]), requires_grad=True)
10 b = Variable(torch.Tensor([0]), requires_grad=True)
11 epochs = 100
12 costs = []
13 lr = 0.1
14 print("before training, predict of x = 1.5 is:")
15 print("y_pred = ", float(w.data*1.5 + b.data > 0))
16 
17 #模型训练
18 for epoch in range(epochs):
19     #计算梯度
20     A = 1/(1+torch.exp(-(w*x_data+b))) #逻辑回归函数
21     J = -torch.mean(y_data*torch.log(A) + (1-y_data)*torch.log(1-A))  #逻辑回归损失函数
22     #J = -torch.mean(y_data*torch.log(A) + (1-y_data)*torch.log(1-A)) +alpha*w**2
23     #基础类进行正则化，加上L2范数
24     costs.append(J.data)
25     J.backward()  #自动反向传播
26 
27     #参数更新
28     w.data = w.data - lr*w.grad.data
29     w.grad.data.zero_()
30     b.data = b.data - lr*b.grad.data
31     b.grad.data.zero_()
32 
33 print("after training, predict of x = 1.5 is:")
34 print("y_pred =", float(w.data*1.5+b.data > 0))
35 print(w.data, b.data)

2.用PyTorch类实现Logistic regression,torch.nn.module写网络结构

 1 import torch
 2 from torch.autograd import Variable
 3 
 4 x_data = Variable(torch.Tensor([[0.6], [1.0], [3.5], [4.0]]))
 5 y_data = Variable(torch.Tensor([[0.], [0.], [1.], [1.]]))
 6 
 7 class Model(torch.nn.Module):
 8     def __init__(self):
 9         super(Model, self).__init__()
10         self.linear = torch.nn.Linear(1, 1) 
11         self.sigmoid = torch.nn.Sigmoid()  ###### **sigmoid**
12 
13     def forward(self, x):
14         y_pred = self.sigmoid(self.linear(x))
15         return y_pred
16 
17 
18 model = Model()
19 
20 
21 criterion = torch.nn.BCELoss(size_average=True)        #损失函数
22 optimizer = torch.optim.SGD(model.parameters(), lr=0.01)   # 随机梯度下降
23 
24 
25 for epoch in range(500):
26     # Forward pass
27     y_pred = model(x_data)
28 
29     
30     loss = criterion(y_pred, y_data)
31     if epoch % 20 == 0:
32         print(epoch, loss.item())
33 
34     #梯度归零
35     optimizer.zero_grad()
36     # 反向传播
37     loss.backward()
38     # update weights
39     optimizer.step()
40 
41 hour_var = Variable(torch.Tensor([[0.5]]))
42 print("predict (after training)", 0.5, model.forward(hour_var).data[0][0])
43 hour_var = Variable(torch.Tensor([[7.0]]))
44 print("predict (after training)", 7.0, model.forward(hour_var).data[0][0])

参考：https://blog.csdn.net/ZZQsAI/article/details/90216593