python MLPRegressor神經網絡迴歸預測

'''載入數據'''
from sklearn import datasets
boston = datasets.load_boston()
x,y = boston.data,boston.target
'''引入標準化函數'''
from sklearn import preprocessing
x_MinMax = preprocessing.MinMaxScaler()
y_MinMax = preprocessing.MinMaxScaler()

''' 將 y 轉換成 列 '''
import numpy as np
y = np.array(y).reshape(len(y),1)
'''標準化'''
x = x_MinMax.fit_transform(x)
y = y_MinMax.fit_transform(y)

''' 按二八原則劃分訓練集和測試集 '''
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)

'''模型構建'''
from sklearn.neural_network import MLPRegressor
fit1 = MLPRegressor(
        hidden_layer_sizes=(100,50), activation='relu',solver='adam',
        '''第一個隱藏層有100個節點，第二層有50個，激活函數用relu，梯度下降方法用adam'''
        alpha=0.01,max_iter=200)
        '''懲罰係數爲0.01，最大迭代次數爲200'''
print ("fitting model right now")
fit1.fit(x_train,y_train)
pred1_train = fit1.predict(x_train)
'''計算訓練集 MSE'''
from sklearn.metrics import mean_squared_error
mse_1 = mean_squared_error(pred1_train,y_train)
print ("Train ERROR = ", mse_1)
'''計算測試集mse'''
pred1_test = fit1.predict(x_test)
mse_2 = mean_squared_error(pred1_test,y_test)
print ("Test ERROR = ", mse_2)

'''結果可視化'''
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()

結果如下：