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 pd
 
data = 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 = 0.0001
 
w = 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) + b
        loss = (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_rate
            b.data -= b.grad.data * learning_rate
 
plt.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