PyTorch实现Logistic回归对多元高斯分布进行分类实战代码

我可以为您提供这个问题的实战代码，这是一个使用PyTorch实现对多元高斯分布进行分类的Logistic回归代码：

import torch
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt

#生成数据集
np.random.seed(1)
num_observations = 5000
x1 = np.random.multivariate_normal([0, 0], [[1, .75],[.75, 1]], num_observations)
x2 = np.random.multivariate_normal([1, 4], [[1, .75],[.75, 1]], num_observations)
simulated_separableish_features = np.vstack((x1, x2)).astype(np.float32)
simulated_labels = np.hstack((np.zeros(num_observations),
                              np.ones(num_observations)))

#绘制数据集
fig, ax = plt.subplots()
ax.scatter(simulated_separableish_features[:, 0], simulated_separableish_features[:, 1],
           c = simulated_labels, alpha=.4)

#将数据集转换成Tensor
features = Variable(torch.from_numpy(simulated_separableish_features))
labels = Variable(torch.from_numpy(simulated_labels))

#定义模型
model = torch.nn.Sequential(
    torch.nn.Linear(2, 1),
    torch.nn.Sigmoid()
)

#定义误差函数和优化器
loss_fn = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

#训练模型
for epoch in range(500):
    #向前传播
    y_pred = model(features)
    #计算误差
    loss = loss_fn(y_pred, labels)
    #清除梯度
    optimizer.zero_grad()
    #反向传播
    loss.backward()
    #更新参数
    optimizer.step()

#预测
predicted = model(features).data.numpy().flatten()
plt.plot(simulated_separableish_features[:, 0], simulated_separableish_features[:, 1],
         'go', alpha=.3)
plt.plot(simulated_separableish_features[:, 0], simulated_separableish_features[:, 1],
         'ro', alpha=.3)
plt.legend(['False', 'True'])
plt.show()

希望这段代码能够帮助您解决问题。