线性回归预测波士顿房价 loss为NAN原因画散点图找特征与标签的关系

波士顿房价csv文件

链接: https://pan.baidu.com/s/1uz6oKs7IeEzHdJkfrpiayg?pwd=vufb 提取码: vufb

代码

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

从CSV中取出数据集

# 加载数据,第一行是无用行，直接跳过
boston = pd.read_csv('../data/boston_house_prices.csv',skiprows=[0])
# 共有14列，前面十三列是特征，最后一列是价格
boston

在这里插入图片描述

取最后一列设置为labels,前面所有列为features

# 最后一列作为labels，把前面十三列的内容作为features
# 直接让最后一列出栈，boston剩下前面13列
labels = boston.pop('MEDV')
features = boston

画散点图，看特征与房价的关系，如果是线性关系，则说明该特征与标签存在一定的相关性。选出与labels相关的特征，作为最终的features

# 看各个特征与房价的散点图
data_xTitle = ['CRIM','ZN','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B', 'LSTAT']
# 设置5行，3列 =15个子图
fig, a = plt.subplots(5, 3)
m = 0
for i in range(0, 5):
    if i == 4:
        a[i][0].scatter(features[str(data_xTitle[m])], labels, s=30, edgecolor='white')
        a[i][0].set_title(str(data_xTitle[m]))
    else:
        for j in range(0, 3):
            a[i][j].scatter(features[str(data_xTitle[m])], labels, s=30, edgecolor='white')
            a[i][j].set_title(str(data_xTitle[m]))
            m = m + 1
plt.show()
# 由下面的图可以看出CRIM，RM，LSTAT 与y是线性的关系，所以选择这三个特征作为特征值。

在这里插入图片描述

# CRIM，RM，LSTAT 与y是线性的关系，所以选择这三个特征作为特征值。
features = features[['LSTAT','CRIM','RM']]

把数据格式转为tensor

features = torch.tensor(np.array(features)).to(torch.float32)
labels = torch.tensor(np.array(labels)).to(torch.float32)
features.shape，labels.shape

(torch.Size([506, 13]), torch.Size([506]))

定义线性回归，损失函数，优化函数

# 制定线性回归模型
def linreg(X,w,b):
    return torch.matmul(X,w) + b
    
# 定义损失函数
def squared_loss(y_hat,y):
    return (y_hat - y.reshape(y_hat.shape)) **2 /2
    
# 定义优化函数
def sgd(params,lr,batch_size):
    '''小批量随机梯度下降'''
    with torch.no_grad():
        for param in params:
            param -= lr * param.grad / batch_size
            param.grad.zero_()

data_iter函数，按批次取数据

def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    # 这些样本是随机读取的,没有特定的顺序
    random.shuffle(indices)
    for i in range(0, num_examples, batch_size):
        batch_indices = torch.tensor(indices[i: min(i + batch_size, num_examples)])
        yield features[batch_indices], labels[batch_indices]

设置参数

w = torch.normal(0, 0.01, size=(features.shape[1],1), requires_grad=True)
b = torch.zeros(1, requires_grad=True)
lr = 0.03
# lr = 0.0001
num_epochs = 100
net = linreg
loss = squared_loss
batch_size = 10

w和b的shape为：
torch.Size([3, 1])
torch.Size([1])

开始训练

for epoch in range(num_epochs):
    for X, y in data_iter(batch_size, features, labels):
        l = loss(net(X, w, b), y)
        # X和y的小批量损失
        # 因为l形状是(batch_size,1),而不是一个标量。l中的所有元素被加到一起,
        # 并以此计算关于[w,b]的梯度
        l.sum().backward()
        sgd([w, b], lr, batch_size)
    # 使用参数的梯度更新参数
    with torch.no_grad():
        train_l = loss(net(features, w, b), labels)
        print(f'epoch {epoch + 1}, loss {float(train_l.mean()):f}')

当模型的学习率设置为0.03，loss直接变为NAN

epoch 1, loss nan
epoch 2, loss nan
epoch 3, loss nan
epoch 4, loss nan
epoch 5, loss nan
epoch 6, loss nan
epoch 7, loss nan
epoch 8, loss nan
epoch 9, loss nan
epoch 10, loss nan
epoch 11, loss nan
epoch 12, loss nan
epoch 13, loss nan
epoch 14, loss nan
epoch 15, loss nan
epoch 16, loss nan
epoch 17, loss nan
epoch 18, loss nan
epoch 19, loss nan
epoch 20, loss nan
epoch 21, loss nan
epoch 22, loss nan
epoch 23, loss nan
epoch 24, loss nan
epoch 25, loss nan
epoch 26, loss nan
epoch 27, loss nan
epoch 28, loss nan
epoch 29, loss nan
epoch 30, loss nan
epoch 31, loss nan
epoch 32, loss nan
epoch 33, loss nan
epoch 34, loss nan
epoch 35, loss nan
epoch 36, loss nan
epoch 37, loss nan
epoch 38, loss nan
epoch 39, loss nan
epoch 40, loss nan
epoch 41, loss nan
epoch 42, loss nan
epoch 43, loss nan
epoch 44, loss nan
epoch 45, loss nan
epoch 46, loss nan
epoch 47, loss nan
epoch 48, loss nan
epoch 49, loss nan
epoch 50, loss nan
epoch 51, loss nan
epoch 52, loss nan
epoch 53, loss nan
epoch 54, loss nan
epoch 55, loss nan
epoch 56, loss nan
epoch 57, loss nan
epoch 58, loss nan
epoch 59, loss nan
epoch 60, loss nan
epoch 61, loss nan
epoch 62, loss nan
epoch 63, loss nan
epoch 64, loss nan
epoch 65, loss nan
epoch 66, loss nan
epoch 67, loss nan
epoch 68, loss nan
epoch 69, loss nan
epoch 70, loss nan
epoch 71, loss nan
epoch 72, loss nan
epoch 73, loss nan
epoch 74, loss nan
epoch 75, loss nan
epoch 76, loss nan
epoch 77, loss nan
epoch 78, loss nan
epoch 79, loss nan
epoch 80, loss nan
epoch 81, loss nan
epoch 82, loss nan
epoch 83, loss nan
epoch 84, loss nan
epoch 85, loss nan
epoch 86, loss nan
epoch 87, loss nan
epoch 88, loss nan
epoch 89, loss nan
epoch 90, loss nan
epoch 91, loss nan
epoch 92, loss nan
epoch 93, loss nan
epoch 94, loss nan
epoch 95, loss nan
epoch 96, loss nan
epoch 97, loss nan
epoch 98, loss nan
epoch 99, loss nan
epoch 100, loss nan

当模型的学习率设置为0.0001，loss正常，模型开始收敛

epoch 1, loss 141.555878
epoch 2, loss 115.449852
epoch 3, loss 101.026237
epoch 4, loss 90.287994
epoch 5, loss 81.646828
epoch 6, loss 74.384491
epoch 7, loss 68.148872
epoch 8, loss 62.699074
epoch 9, loss 57.872326
epoch 10, loss 53.601421
epoch 11, loss 49.778000
epoch 12, loss 46.333401
epoch 13, loss 43.253365
epoch 14, loss 40.471313
epoch 15, loss 37.963455
epoch 16, loss 35.711601
epoch 17, loss 33.679176
epoch 18, loss 31.841145
epoch 19, loss 30.203505
epoch 20, loss 28.699686
epoch 21, loss 27.352037
epoch 22, loss 26.142868
epoch 23, loss 25.045834
epoch 24, loss 24.059885
epoch 25, loss 23.171280
epoch 26, loss 22.369287
epoch 27, loss 21.646309
epoch 28, loss 20.998608
epoch 29, loss 20.407761
epoch 30, loss 19.874365
epoch 31, loss 19.396839
epoch 32, loss 18.967056
epoch 33, loss 18.576946
epoch 34, loss 18.234808
epoch 35, loss 17.904724
epoch 36, loss 17.623093
epoch 37, loss 17.360590
epoch 38, loss 17.126835
epoch 39, loss 16.916040
epoch 40, loss 16.727121
epoch 41, loss 16.555841
epoch 42, loss 16.401901
epoch 43, loss 16.264545
epoch 44, loss 16.145824
epoch 45, loss 16.026453
epoch 46, loss 15.927325
epoch 47, loss 15.830773
epoch 48, loss 15.748351
epoch 49, loss 15.672281
epoch 50, loss 15.606522
epoch 51, loss 15.546185
epoch 52, loss 15.490641
epoch 53, loss 15.458157
epoch 54, loss 15.395338
epoch 55, loss 15.359412
epoch 56, loss 15.331330
epoch 57, loss 15.284848
epoch 58, loss 15.264071
epoch 59, loss 15.238921
epoch 60, loss 15.206428
epoch 61, loss 15.184341
epoch 62, loss 15.190187
epoch 63, loss 15.144171
epoch 64, loss 15.127305
epoch 65, loss 15.115336
epoch 66, loss 15.111353
epoch 67, loss 15.098548
epoch 68, loss 15.077714
epoch 69, loss 15.075640
epoch 70, loss 15.072990
epoch 71, loss 15.051690
epoch 72, loss 15.046121
epoch 73, loss 15.038815
epoch 74, loss 15.038069
epoch 75, loss 15.027984
epoch 76, loss 15.028069
epoch 77, loss 15.030132
epoch 78, loss 15.015227
epoch 79, loss 15.014658
epoch 80, loss 15.010786
epoch 81, loss 15.005883
epoch 82, loss 15.007875
epoch 83, loss 15.003115
epoch 84, loss 15.015619
epoch 85, loss 14.996306
epoch 86, loss 15.008889
epoch 87, loss 14.993307
epoch 88, loss 14.997282
epoch 89, loss 14.990996
epoch 90, loss 14.991257
epoch 91, loss 14.997286
epoch 92, loss 14.989521
epoch 93, loss 14.987417
epoch 94, loss 14.989147
epoch 95, loss 14.989621
epoch 96, loss 14.984948
epoch 97, loss 14.984961
epoch 98, loss 14.984855
epoch 99, loss 14.983346
epoch 100, loss 14.999675