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