利用keras自带房价数据集进行房价预测

 import numpy as np
 from keras.datasets import boston_housing
 from keras import layers
 from keras import models
 from keras import optimizers
 from keras.utils.np_utils import to_categorical
 import matplotlib.pyplot as plt

 def main():
     (train_data, train_labels), (test_data, test_labels) = boston_housing.load_data()
     mean = train_data.mean(axis = 0)# 通过(x-均值)/方差 把数据变为均值为0,方差为1
     std = train_data.std(axis = 0)
     train_data -= mean
     train_data /= std
     test_data -= mean
     test_data /= std

     model = models.Sequential()
     model.add(layers.Dense(64, activation = 'relu', input_shape = (train_data.shape[1], )))
     model.add(layers.Dense(64, activation = 'relu'))
     model.add(layers.Dense(1))

     model.compile(optimizer = 'rmsprop', loss = 'mse', metrics = ['mae'])# mae: 平均绝对误差

     model.fit(train_data, train_labels, epochs = 80, batch_size = 16)

     _, mae = model.evaluate(test_data, test_labels)
     print(mae)

 if __name__ == "__main__":
     main()

最后预测的房价还是和实际价格相差约 2000美元左右