modelfile = 'D:/data/example03/1-net.model' #模型保存路径
data = pd.read_excel(inputfile) #读取数据
feature = ['x1', 'x4'] #特征所在列
data_train = data.loc[range(1994, 2010)].copy() #取2010年前的数据建模
data_mean = data_train.mean()
data_std = data_train.std()
data_train = (data_train - data_mean) / data_std #数据标准化
x_train = data_train[feature].as_matrix() #特征数据
y_train = data_train['y'].as_matrix() #标签数据
from keras.models import Sequential
from keras.layers.core import Dense, Activation
model = Sequential() #建立模型
model.add(Dense(input_dim=2, output_dim=6))
model.add(Activation('relu')) #用relu函数作为激活函数，能够大幅提供准确度
model.add(Dense(input_dim=6, output_dim=1))
model.compile(loss='mean_squared_error', optimizer='adam') #编译模型
model.fit(x_train, y_train, nb_epoch=10000, batch_size=16) #训练模型，学习一万次
model.save_weights(modelfile) #保存模型参数
#预测，并还原结果。
x = ((data[feature] - data_mean[feature]) / data_std[feature]).as_matrix()
data[u'y_pred'] = model.predict(x) * data_std['y'] + data_mean['y']
data.to_excel(outputfile)
import matplotlib.pyplot as plt #画出预测结果图
p = data[['y', 'y_pred']].plot(subplots=True, style=['b-o', 'r-*'])
plt.show()
