深度学习之用PyTorch实现逻辑回归

0.1 学习视频源于：b站：刘二大人《PyTorch深度学习实践》

0.2 本章内容为自主学习总结内容，若有错误欢迎指正！

代码（类比线性回归）：

# 调用库
import torch
import torch.nn.functional as F# 数据准备
x_data = torch.Tensor([[1.0], [2.0], [3.0]])  # 训练集输入值
y_data = torch.Tensor([[0], [0], [1]])  # 训练集输出值# 定义逻辑回归模型
class LogisticRegressionModel(torch.nn.Module):def __init__(self):super(LogisticRegressionModel, self).__init__()  # 调用父类构造函数self.linear = torch.nn.Linear(1, 1)  # 实例化torch库nn模块的Linear类，特征一维，输出一维def forward(self, x):"""前馈运算:param x: 输入值:return: 线性回归预测结果"""y_pred = F.sigmoid(self.linear(x))return y_predmodel = LogisticRegressionModel()  # 实例化criterion = torch.nn.BCELoss(size_average=False)  # 损失函数
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)  # 优化器——梯度下降SGD# 训练过程
for epoch in range(1000):  # epoch：训练轮次y_pred = model(x_data)loss = criterion(y_pred, y_data)print(epoch, loss.item())optimizer.zero_grad()  # 梯度归零loss.backward()  # 反向传播optimizer.step()  # 权重自动更新print("w = ", model.linear.weight.item())
print("b = ", model.linear.bias.item())# 预测过程
x_test = torch.Tensor([[3.5]])
y_test = model(x_test)
print("y_pred = ", y_test.data)

结果：