tensorflow2.0——波士顿房价数据与多个特征的关系（多元函数的预测）

import tensorflow as tf
import matplotlib.pylab as plt
import numpy as np

# 调用数据
boston_house = tf.keras.datasets.boston_housing
(train_x, train_y), (test_x, test_y) = boston_house.load_data(test_split=0.1)
x1 = train_x[:, 5]                # 房间数
x2 = train_x[:,7]                       #   另一个参数
#   样本数据归一化
x1 = (x1 - x1.min())/ (x1.max() - x1.min())
x2 = (x2 - x2.min())/ (x2.max() - x2.min())
#   维度转换
X = np.stack((x1,x2),axis=1)                    #   转化成一个二维数组
# print(\'X:\',X,X.shape)
Y = train_y.reshape(-1,1)                    #   将房价y转化为列向量

#   超参数
iter =1000              #   迭代次数
learn_rate = 0.01      #   学习率
#   设置模型参数初始值
w = np.random.randn(2,1)
W = tf.Variable(w)
loss_list = []
# print(\'W:{},W.shape:{}\'.format(W,W.shape))
for i in range(iter):
    with tf.GradientTape() as tape:
        pre_price = tf.matmul(X,W)
        loss = 0.5 * (tf.reduce_mean(pow(Y - pre_price,2)))
    loss_list.append(loss)
    dloss_dw = tape.gradient(loss,W)                        #   偏导也是一个2维向量
    # print(\'dloss_dw:\',dloss_dw)
    W.assign_sub(learn_rate * dloss_dw)
    if i % 100 == 0:
        print(\'i:{},loss:{},w:{}\'.format(i,loss,w))
#   画图
plt.rcParams["font.family"] = \'SimHei\'              # 将字体改为中文
plt.rcParams[\'axes.unicode_minus\'] = False          # 设置了中文字体默认后，坐标的"-"号无法显示，设置这个参数就可以避免
plt.plot(loss_list,label = \'损失值\')
plt.legend()
plt.show()