PyTorch学习5《PyTorch深度学习实践》——逻辑回归[对数几率回归]（Logistic Regression）

Posted 2023-03-04 小龙呀

一、基础介绍
Logistic回归准确来说不是回归，而是一个分类方法。如下图，Logistic回归就比线性回归多了一个Sigmoid函数（一般叫做激活函数，为非线性变换，当然激活函数有很多）


二、Binary Cross Entropy
二元交叉熵是交叉熵的一种特例，常用于二分类问题。
有关于BCEloss的解释可以看这个视频：吴恩达深度学习——logistic损失函数的解释

三、程序实现

import torch

# 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 = torch.sigmoid(self.linear(x))
        return y_pred

model = LogisticRegressionModel()
print(model)

# construct loss and optimizer
criterion = torch.nn.BCELoss(reduction='sum')
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)
    if (epoch + 1) % 100 == 0:
        print(f"Epoch [epoch + 1/1000], Loss: 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)