PyTorch深度学习实践——Logistic回归_pytorch之优化头歌

import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
 
x_data = torch.Tensor([[1.0], [2.0], [3.0]])
y_data = torch.Tensor([[0], [0], [1]])  # 现在的输入y为分类，故只为0或1
 
# ----------------------------------------------------------------准备数据集
 
 
class LogisticRegressionModel(torch.nn.Module):
    def __init__(self):
        super(LogisticRegressionModel, self).__init__()
        self.linear = torch.nn.Linear(1, 1)  # 先用linear进行线性变换，输入维度为1，输出维度为1
        # sigmoid函数中没有参数，故不需要在构造函数中初始化，因为没有参数可供训练，直接调用即可
 
    def forward(self, x):
        y_pred = F.torch.sigmoid(self.linear(x))  # 再用sigmoid函数处理线性变换后的结果，作为最后的输出
        return y_pred
 
 
model = LogisticRegressionModel()
# --------------------------------------------------------------设计模型
 
criterion = torch.nn.BCELoss(reduction='sum')  # BCE损失函数
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# ------