python实现随机梯度下降法

import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import axes3dfrom matplotlib import style  #构造数据def get_data(sample_num=1000): """ 拟合函数为 y = 5*x1 + 7*x2 :return: """ x1 = np.linspace(0, 9, sample_num) x2 = np.linspace(4, 13, sample_num) x = np.concatenate(([x1], [x2]), axis=0).T y = np.dot(x, np.array([5, 7]).T)  return x, y#梯度下降法def SGD(samples, y, step_size=2, max_iter_count=1000): """ :param samples: 样本 :param y: 结果value :param step_size: 每一接迭代的步长 :param max_iter_count: 最大的迭代次数 :param batch_size: 随机选取的相对于总样本的大小 :return: """ #确定样本数量以及变量的个数初始化theta值  m, var = samples.shape theta = np.zeros(2) y = y.flatten() #进入循环内 loss = 1 iter_count = 0 iter_list=[] loss_list=[] theta1=[] theta2=[] #当损失精度大于0.01且迭代此时小于最大迭代次数时，进行 while loss > 0.01 and iter_count < max_iter_count:  loss = 0  #梯度计算  theta1.append(theta[0])  theta2.append(theta[1])    #样本维数下标  rand1 = np.random.randint(0,m,1)  h = np.dot(theta,samples[rand1].T)  #关键点，只需要一个样本点来更新权值  for i in range(len(theta)):   theta[i] =theta[i] - step_size*(1/m)*(h - y[rand1])*samples[rand1,i]  #计算总体的损失精度，等于各个样本损失精度之和  for i in range(m):   h = np.dot(theta.T, samples[i])   #每组样本点损失的精度   every_loss = (1/(var*m))*np.power((h - y[i]), 2)   loss = loss + every_loss   print("iter_count: ", iter_count, "the loss:", loss)    iter_list.append(iter_count)  loss_list.append(loss)    iter_count += 1 plt.plot(iter_list,loss_list) plt.xlabel("iter") plt.ylabel("loss") plt.show() return theta1,theta2,theta,loss_list def painter3D(theta1,theta2,loss): style.use('ggplot') fig = plt.figure() ax1 = fig.add_subplot(111, projection='3d') x,y,z = theta1,theta2,loss ax1.plot_wireframe(x,y,z, rstride=5, cstride=5) ax1.set_xlabel("theta1") ax1.set_ylabel("theta2") ax1.set_zlabel("loss") plt.show()  if __name__ == '__main__': samples, y = get_data() theta1,theta2,theta,loss_list = SGD(samples, y) print(theta) # 会很接近[5, 7]  painter3D(theta1,theta2,loss_list)