Task3.PyTorch实现Logistic regression

1.PyTorch基础实现代码

 import torch
 from torch.autograd import Variable

 torch.manual_seed(2)
 x_data = Variable(torch.Tensor([[1.0], [2.0], [3.0], [4.0]]))
 y_data = Variable(torch.Tensor([[0.0], [0.0], [1.0], [1.0]]))

 #初始化
 w = Variable(torch.Tensor([-1]), requires_grad=True)
 b = Variable(torch.Tensor([0]), requires_grad=True)
 epochs = 100
 costs = []
 lr = 0.1
 print("before training, predict of x = 1.5 is:")
 print("y_pred = ", float(w.data*1.5 + b.data > 0))

 #模型训练
 for epoch in range(epochs):
     #计算梯度
     A = 1/(1+torch.exp(-(w*x_data+b))) #逻辑回归函数
     J = -torch.mean(y_data*torch.log(A) + (1-y_data)*torch.log(1-A))  #逻辑回归损失函数
     #J = -torch.mean(y_data*torch.log(A) + (1-y_data)*torch.log(1-A)) +alpha*w**2
     #基础类进行正则化，加上L2范数
     costs.append(J.data)
     J.backward()  #自动反向传播

     #参数更新
     w.data = w.data - lr*w.grad.data
     w.grad.data.zero_()
     b.data = b.data - lr*b.grad.data
     b.grad.data.zero_()

 print("after training, predict of x = 1.5 is:")
 print("y_pred =", float(w.data*1.5+b.data > 0))
 print(w.data, b.data)

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

 import torch
 from torch.autograd import Variable

 x_data = Variable(torch.Tensor([[0.6], [1.0], [3.5], [4.0]]))
 y_data = Variable(torch.Tensor([[0.], [0.], [1.], [1.]]))

 class Model(torch.nn.Module):
     def __init__(self):
         super(Model, self).__init__()
         self.linear = torch.nn.Linear(1, 1)
         self.sigmoid = torch.nn.Sigmoid()  ###### **sigmoid**

     def forward(self, x):
         y_pred = self.sigmoid(self.linear(x))
         return y_pred

 model = Model()

 criterion = torch.nn.BCELoss(size_average=True)        #损失函数
 optimizer = torch.optim.SGD(model.parameters(), lr=0.01)   # 随机梯度下降

 for epoch in range(500):
     # Forward pass
     y_pred = model(x_data)

     loss = criterion(y_pred, y_data)
     if epoch % 20 == 0:
         print(epoch, loss.item())

     #梯度归零
     optimizer.zero_grad()
     # 反向传播
     loss.backward()
     # update weights
     optimizer.step()

 hour_var = Variable(torch.Tensor([[0.5]]))
 print("predict (after training)", 0.5, model.forward(hour_var).data[0][0])
 hour_var = Variable(torch.Tensor([[7.0]]))
 print("predict (after training)", 7.0, model.forward(hour_var).data[0][0])

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