python神经网络回归_python MLPRegressor神经网络回归预测

'''载入数据'''

from sklearn import datasets

boston = datasets.load_boston()

x,y = boston.data,boston.target

'''引入标准化函数'''

from sklearn import preprocessing

x_MinMax = preprocessing.MinMaxScaler()

y_MinMax = preprocessing.MinMaxScaler()

''' 将 y 转换成 列 '''

import numpy as np

y = np.array(y).reshape(len(y),1)

'''标准化'''

x = x_MinMax.fit_transform(x)

y = y_MinMax.fit_transform(y)

''' 按二八原则划分训练集和测试集 '''

from sklearn.model_selection import train_test_split

np.random.seed(2019)

x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2)

'''模型构建'''

from sklearn.neural_network import MLPRegressor

fit1 = MLPRegressor(

hidden_layer_sizes=(100,50), activation='relu',solver='adam',

'''第一个隐藏层有100个节点，第二层有50个，激活函数用relu，梯度下降方法用adam'''

alpha=0.01,max_iter=200)

'''惩罚系数为0.01，最大迭代次数为200'''

print ("fitting model right now")

fit1.fit(x_train,y_train)

pred1_train = fit1.predict(x_train)

'''计算训练集 MSE'''

from sklearn.metrics import mean_squared_error

mse_1 = mean_squared_error(pred1_train,y_train)

print ("Train ERROR = ", mse_1)

'''计算测试集mse'''

pred1_test = fit1.predict(x_test)

mse_2 = mean_squared_error(pred1_test,y_test)

print ("Test ERROR = ", mse_2)

'''结果可视化'''

import matplotlib.pyplot as plt

xx=range(0,len(y_test))

plt.figure(figsize=(8,6))

plt.scatter(xx,y_test,color="red",label="Sample Point",linewidth=3)

plt.plot(xx,pred1_test,color="orange",label="Fitting Line",linewidth=2)

plt.legend()

plt.show()

结果如下：