6-逻辑斯蒂回归

注意torch.Tensor()和torch.tensor()的区别

点击查看代码

import torch
import torch.nn.functional as F

x_data = torch.Tensor([[1.0], [2.0], [3.0]])
y_data = torch.Tensor([[0], [0], [1]]) # 注意torch.tensor()和torch.Tensor()的区别

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 = F.sigmoid(self.linear(x))
        return y_pred

model = LogisticRegressionModel()

# 定义损失函数和优化器
criterion = torch.nn.BCELoss(reduction='sum') # 不求均值
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(100):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print('epoch=',epoch, 'loss=',loss)

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