# -*- coding:utf-8 -*-
# 房价预测，回归任务
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import pandas as pd
import numpy as np
from keras import callbacks
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import KFold, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
seed = 7
np.random.seed(seed)
def main():
house_df = pd.read_csv('./data/housing.csv', sep='\s+', header=None)
hose_set = house_df.values
# print(hose_set)
x = hose_set[:, 0:13]
y = hose_set[:, 13]
# print(y)
# tbcallback=callbacks.TensorBoard(log_dir='./logs',histogram_freq=0, write_graph=True, write_images=True)
estimators = []
estimators.append(('mlp', KerasRegressor(build_fn=build_model, epochs=512, batch_size=32, verbose=1)))
pipeline = Pipeline(estimators)
kfold = KFold(n_splits=10, random_state=seed)
# results = cross_val_score(estimator, x, y, cv=kfold)
scores = cross_val_score(pipeline, x, y, cv=kfold)
print('\n')
print("Results: %.2f (%.2f) MSE" % (scores.mean(), scores.std()))
def build_model():
model = Sequential()
model.add(Dense(32, input_dim=13,kernel_initializer='normal', activation='relu'))
model.add(Dense(16, kernel_initializer='normal',activation='relu'))
model.add(Dense(8, kernel_initializer='normal',activation='linear'))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(optimizer='rmsprop', loss='mae', metrics=['accuracy'])
return model
if __name__ == '__main__':
main()
