PyTorch学习5：Logistic回归

文章目录

• 前言
• 一、分类问题简介
• 二、示例
• • 1.示例步骤
  • 2.示例代码
• 总结

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前言

介绍利用PyTorch实现Logistic回归的分类问题

一、分类问题简介

分类问题的输出为属于每一个类别的概率，概率值最大的即为所属类别。最常见的Sigmoid函数：Logistic函数。

二、示例

1.示例步骤

1.构建模型 class LogisticRegressionModel(torch.nn.Module):

2.定义损失函数和优化器

criterion = torch.nn.BCELoss(size_average=False)

optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

3.训练过程

2.示例代码

代码如下（示例）：

import torch
import matplotlib.pyplot as plt
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))
        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)
epoch_list = []
loss_list = []
# training cycle forward, backward, update
for epoch in range(10000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    epoch_list.append(epoch)
    loss_list.append(loss.item())

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)
plt.plot(epoch_list, loss_list)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.show()

得到如下结果：

总结

PyTorch学习5：Logistic回归