import torch
from sklearn.datasets import make_regression
import matplotlib.pyplot as plt
import random
#1.构建数据
#构建数据集
def create_dataset():x,y,coef=make_regression(n_samples=100,n_features=1,random_state=0,noise=10,coef=True,bias=14.5)#将构建数据转换为张量类型x=torch.tensor(x)y=torch.tensor(y)return x,y#构建数据加载器
def data_loader(x,y, batch_size):#计算下样本的数量data_len = len(y)#构建数据索引data_index=list(range(data_len))random.shuffle(data_index)#计算总的batch数量batch_number=data_len//batch_sizefor idx in range(batch_number):start=idx+batch_sizeend=start+batch_sizebatch_train_x=x[start:end]batch_train_y=y[start:end]yield batch_train_x,batch_train_ydef test01():x,y=create_dataset()plt.scatter(x,y)plt.show()for x,y in data_loader(x,y,batch_size=10):print(y)
#2.假设函数、损失函数、优化方法
#损失函数：平均损失
#优化方法：梯度下降
#假设函数
w=torch.tensor(0.1,requires_grad=True,dtype=torch.float64)
b=torch.tensor(0.1,requires_grad=True,dtype=torch.float64)def linear_regression(x):return w*x+b#损失函数
def square_loss(y_pred,y_true):return torch.square(y_pred - y_true)#优化方法
def sqd(lr=1e-2):#除以16是使用的是批次样本的平均梯度w.data=w.data-lr*w.grad.data/16b.data=b.data-lr*b.grad.data/16if __name__ == '__main__':test01()
