【Pytorch深度学习开发实践学习】B站刘二大人课程笔记整理lecture06 Logistic回归

课程网址
Pytorch深度学习实践

部分课件内容：

import torch

x_data =torch.tensor([[1.0],[2.0],[3.0]])
y_data =torch.tensor([[0.0],[0.0],[1.0]])

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(reduction='sum')
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)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    print(epoch,loss.data)