参考：
主要参考 课时5 简单回归问题-2_哔哩哔哩_bilibili，
系统的回顾一下pytorch
目录
1： 简单回归问题
2： 回归问题实战
一 简单回归问题（Linear Regression）
根据预测值,或者标签值不同
线性回归问题
损失函数
参数学习：
梯度下降原理（泰勒公式展开）
设
二 回归问题实战
数据集
三 例
3.1 训练部分
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 10 21:33:37 2022
@author: cxf
"""
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
from draw import draw_loss
#需要继承data.Dataset
class MyDataset(Dataset):
def __init__(self, data, target):
self.x = data
self.y = target
self.len = self.x.shape[0] #样本个数
def __getitem__(self, index):
x = self.x[index]
y = self.y[index]
return x,y
def __len__(self):
return self.len
#linear regression
class LR:
'''
预测值
args
w: 权重系数
b: 偏置系数
'''
def predict(self,w,b,x):
predY= torch.mm(w.T,x)+b
return predY
'''
梯度更新
args
w_cur: 权重系数
b_cur 偏置
trainX: 训练数据集
trainY: 标签集
'''
def step_gradient(self,w_cur,b_cur, trainX,trainY):
w_gradient = 0
b_gradient = 0
m = trainX.shape[0]
N = float(m)
for i in range(0,m):
x = trainX[i].view(self.n,1)
y = trainY[i]
predY = self.predict(w_cur,b_cur,x)
delta = predY - y
b_gradient +=(2/N)*delta
w_gradient +=(2/N)*delta*x
new_b = b_cur- self.learnRate*b_gradient
new_w = w_cur- self.learnRate*w_gradient
return new_w,new_b
'''
梯度下降训练
args
dataX: 数据集
dataY: 标签集
'''
def train(self,dataX,dataY):
y_train_loss =[]
b_cur = torch.zeros([1,1],dtype=torch.float)
w_cur = torch.rand((self.n,1),dtype=torch.float)
trainData = MyDataset(dataX, dataY)
train_loader = DataLoader(dataset = trainData, batch_size =self.batch, shuffle = True,drop_last =True)
for epoch in range(self.maxIter):
for step, (batch_x, batch_y) in enumerate(train_loader):
w,b = self.step_gradient(w_cur, b_cur, batch_x,batch_y)
w_cur = w
b_cur = b
loss = self.compute_error(w, b, dataX, dataY)
#print("\n epoch: ",epoch,"\n loss ",loss)
y_train_loss.append(loss)
return y_train_loss
def compute_error(self,w,b, dataX,dataY):
totalError = 0.0
m = len(dataX)
for i in range(0,m):
x = dataX[i].view(self.n,1)
y = dataY[i]
predY = self.predict(w, b, x)
z = predY-y
loss = np.power(z,2)
totalError+=loss
totalError = totalError.numpy()[0,0]
return totalError
'''
加载数据集
'''
def loadData(self):
data = np.genfromtxt("data.csv",delimiter=",")
trainData = data[:,0:-1]
trainLabel = data[:,-1]
x = torch.tensor(trainData, dtype=torch.float)
y = torch.tensor(trainLabel, dtype = torch.float)
self.m ,self.n=x.shape[0],x.shape[1]
print("\n m ",self.m,"\t n",self.n)
return x,y
def __init__(self):
self.w = 0 #权重系数
self.b = 0 #偏置
self.m = 0 #样本个数
self.n = 0 #样本维度
self.batch = 20 #训练用的样本数
self.maxIter = 1000 #最大迭代次数
self.learnRate = 0.01 #学习率
if __name__ == "__main__":
lr = LR()
x,y = lr.loadData()
loss = lr.train(x, y)
draw_loss(loss)
3.2 绘图部分
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 14 20:14:28 2022
@author: cxf
"""
import numpy as np
import matplotlib.pyplot as plt
def draw_loss(y_train_loss):
plt.figure()
x_train_loss = range(len(y_train_loss))
# 去除顶部和右边框框
ax = plt.axes()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
#标签
plt.xlabel('iters')
plt.ylabel('accuracy')
plt.plot(x_train_loss, y_train_loss, linewidth=1, linestyle="solid", label="train loss")
plt.legend()
plt.title('train loss')
plt.show()
3.3 数据部分
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 11 22:17:07 2022
@author: cxf
"""
import numpy as np
import csv
def makeData():
wT = np.array([[1.0,2.0,2.0]])
b = 0.5
Data = np.random.random((200,3))
m,n = np.shape(Data)
trainData =[]
for i in range(m):
x = Data[i].T
y = np.matmul(wT,x)+b
item =list(x)
item.append(y[0])
trainData.append(item)
return trainData
def save(data):
csvFile = open("data.csv",'w',newline='')
wr = csv.writer(csvFile)
m = len(data)
for i in range(m):
wr.writerow(data[i])
csvFile.close()
makeData()
if __name__ =="__main__":
data = makeData()
save(data)