五、PyTorch 深度学习 Logistic回归

Logistic回归

来源：B站 刘二大人

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 using class
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))
        y_pred = torch.sigmoid(self.linear(x))
        return y_pred
model = LogisticRegressionModel()
 
# construct loss and optimizer
# 默认情况下，loss会基于element平均，如果size_average=False的话，loss会被累加。
criterion = torch.nn.BCELoss(size_average = False) 
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
 
# training cycle forward, backward, update
for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())
 
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
 
print('w = ', model.linear.weight.item())
print('b = ', model.linear.bias.item())
 
x_test = torch.Tensor([[4.0]])
y_test = model(x_test)
print('y_pred = ', y_test.data)

详细过程与讲解可以参考错错莫和安好家的小朋友的博客
这篇单纯给自己看的