【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)