pytorch-06.逻辑斯蒂回归

#cross-entropy
#BCELoss 二元交叉熵
import torch
import numpy as np
import matplotlib.pyplot as plt

x_data = torch.Tensor([[1.0],[2.0],[3.0]])
y_data = torch.Tensor([[0],[0],[1]])

class LogisticRegressionModel(torch.nn.Module):
    def __init__(self):
        super(LogisticRegressionModel,self).__init__()
        self.linear = torch.nn.Linear(1,1)

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

model = LogisticRegressionModel()

criterion = torch.nn.BCELoss(size_average=False)
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)

for epoch in range (1000):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print('epoch=', epoch, " loss=", loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# Output weight and bias
print('w=',model.linear.weight.item())
print('b=',model.linear.bias.item())

#--------------------------------------------------------
# Test Model
x_test = torch.Tensor([[4.0]])
y_test = model(x_test)
print('y_pred=',y_test.data.item())


x = np.linspace(0,10,200)   #0到10采了200个点
x_t = torch.Tensor(x).view((200,1))  #变成200行1列的矩阵
y_t = model(x_t)
y = y_t.data.numpy()
plt.plot(x,y)
plt.plot([0,10],[0.5,0.5],c='r') #画了一条红线
plt.xlabel('Hours')
plt.ylabel('Probability of Pass')
plt.grid()
plt.show()