Pytorch入门实例的分解写法

数据集是受教育年限和收入,如下图

代码如下

import torch
import numpy as np
import matplotlib.pyplot as plt
import pandas as pddata = pd.read_csv('./Income.csv')X = torch.from_numpy(data.Education.values.reshape(-1,1).astype(np.float32))
Y = torch.from_numpy(data.Income.values.reshape(-1,1).astype(np.float32))learning_rate = 0.0001w = torch.randn(1,requires_grad=True)
b = torch.zeros(1,requires_grad=True)for epoch in range(50):for x,y in zip(X,Y):y_pred = torch.matmul(x,w) + bloss = (y - y_pred).pow(2).mean()if not w.grad is None:w.grad.data.zero_()if not b.grad is None:b.grad.data.zero_()loss.backward()with torch.no_grad():w.data -= w.grad.data * learning_rateb.data -= b.grad.data * learning_rateplt.scatter(data.Education,data.Income)
plt.plot(X.numpy(),(X.numpy() * w.data.numpy() + b.data.numpy()),c='r')
plt.xlabel('Education')
plt.ylabel('Income')
plt.show()

输出如下