数据集ref：https://raw.githubusercontent.com/justinge/pic-go-for-xbotgo/master/Income1.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))

y_pred = model(x) # 预测
loss = loss_fn(y, y_pred) # 计算损失
opt.zero_grad() # 梯度清零
loss.backward() # 反向传播
opt.step() # 下一次

总的来说

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import matplotlib.pyplot as pltdata = pd.read_csv("/home/data_for_ai_justin/01learn/dataset/Income1.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))
loss_fn = nn.MSELoss()
int_features, out_features = 1, 1
model = nn.Linear(int_features,out_features)
opt = torch.optim.SGD(model.parameters(),lr=0.0001
)
for i in range(5000):for x,y in zip(X,Y):y_pred = model(x)loss = loss_fn(y, y_pred)opt.zero_grad()loss.backward()opt.step()plt.scatter(data.Education, data.Income)
plt.plot(X.numpy(), model(X).data.numpy(),c='r')
plt.savefig("1.jpg")