tensorflow2.0——波士顿房价数据预测（3）

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)
print(train_x.shape,train_y.shape)

#   样本数据归一化
    #   测试样本
X = (train_x - train_x.min(axis = 0)) / (train_x.max(axis = 0) - train_x.min(axis = 0))
    #   训练样本
t_X = (test_x - test_x.min(axis = 0)) / (test_x.max(axis = 0) - test_x.min(axis = 0))
print(\'X:\',X,X.shape)

Y = train_y.reshape(-1,1)                    #   将房价y转化为列向量
    #   测试集
t_Y = test_y.reshape(-1,1)
print(\'Y.shape:\',Y.shape)
#   超参数
iter =20000              #   迭代次数
learn_rate = 0.01      #   学习率
#   设置模型参数初始值
w = np.random.randn(13,1)
W = tf.Variable(w)
loss_list = []
test_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)
        pre_test = tf.matmul(t_X, W)
        loss = 0.5 * (tf.reduce_mean(pow(Y - pre_price,2)))
        test_loss = 0.5 * (tf.reduce_mean(pow(t_Y - pre_test,2)))
    loss_list.append(loss)
    test_loss_list.append(test_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 % 1000 == 0:
        print(\'训练集：i:{},loss:{},W:{}\'.format(i,loss,W))
        print(\'测试集：i:{},test_loss:{},W:{}\'.format(i, test_loss, W))
pre_test = tf.matmul(t_X,W)
test_loss = 0.5 * (tf.reduce_mean(pow(t_Y - pre_test,2)))
print(\'测试集loss为:\',test_loss)
#   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.subplot(221)
plt.plot(loss_list,label = \'训练集损失值\')
plt.legend()
plt.subplot(223)
plt.plot(test_loss_list,label = \'测试集损失值\')
plt.legend()
plt.show()