pytorch深度学习实践_p6_用pytoch实现logistic回归

知识点补充

• torch.nn.BCELoss(size_average=False) 求交叉熵的函数

实现的步骤

1. prepare dataset 准备数据集
2. design model using class 使用类来设计模型
3. constuct loss and optimizer 创建loss 和优化器
4. taining cycle 循环训练

斜体样式整体与linear的实现差不多，就把y^和loss fuction 两处做了修改

完整代码

import torch
import torch.nn.functional as F
#prepare dataset
x_data = torch.Tensor([[1.0], [2.0], [3.0]])
y_data = torch.Tensor([[0], [0], [1]])

#design model

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

    def forward(self, x):
        y_pred = F.sigmoid(self.linear(x))   #在y^外边套一层sigmoid函数
        return y_pred

model = LogistiicRegressionModel()
#constuct loss and optimizer

criterion = torch.nn.BCELoss(size_average=False)    #有MSE改为BCE
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

#Training

for epoch in range(10000):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print(epoch, loss.item())

    optimizer.zero_grad() #对之前的梯度清零
    loss.backward()       #反向传播计算梯度
    optimizer.step()      #更新参数

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

x_test = torch.Tensor([4.0])
y_test_pred = model(x_test)
print("y_pred:", y_test_pred.item())