boston_housing-多分类问题

from keras.datasets import boston_housing import numpy as np from keras import models from keras import layers import matplotlib.pyplot as plt #x,13个特征,一共404条数据 #y,连续值标签，单位是千美圆
(x_train, y_train), (x_test, y_test) = boston_housing.load_data() #对数据作原处理，特征标准化 #每一个特征减去平均值，除以标准差，获得的特征平均值为0，标准差为1
avg = x_train.mean(axis=0) x_train -= avg std = np.std(x_train,axis=0) x_train /=std network = models.Sequential() network.add(layers.Dense(64,activation='relu')) network.add(layers.Dense(64,activation='relu')) network.add(layers.Dense(1)) #mse均方偏差，预测值与目标值差的平方 #mae平均绝对偏差，预测值与目标值差的绝对值
network.compile(optimizer='rmsprop',loss='mse',metrics=['mae']) history = network.fit(x_train,y_train,batch_size=16,epochs=80,validation_split=0.1) mae = history.history['val_mean_absolute_error'] epochs = range(1,81) #loss的图
plt.plot(epochs,mae,'g',label = 'mae') plt.xlabel('epochs') plt.ylabel('mean_absolute_error') #显示图例
plt.legend() plt.show()