笔记1：入门实例

pytorch实现线性回归

导入相关python包

import torch
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from torch import nn
%matplotlib inline

加载数据

data = pd.read_csv('E:/datasets/dataset/Income1.csv')
X = torch.from_numpy(data.Education.values.reshape(-1, 1).astype(np.float32)) #DataFrame转tensor的常用方法
Y = torch.from_numpy(data.Income.values.reshape(-1, 1).astype(np.float32))

定义模型

model = nn.Linear(in_features = 1, out_features = 1) # w * input + b 等价于 model(input)
loss_func = nn.MSELoss() # 损失函数
optimizer = torch.optim.SGD(params = model.parameters(), lr = 0.0001)

训练模型

for epoch in range(5000):
    for x, y in zip(X, Y):
        y_pred = model(x)             # 使用模型预测
        loss   = loss_func(y, y_pred) # 根据预测结果计算损失
        optimizer.zero_grad()         # 把变量梯度清 0
        loss.backward()               # 求解梯度
        optimizer.step()              # 优化模型参数

查看训练结果

model.weight, model.bias

plt.scatter(data.Education, data.Income)
plt.plot(X.numpy(), model(X).data.numpy(), c = 'r')
#model(X).data是将tensor取出，否则会带着grad和grad_fn