scikit-learn MLPRegressor prediction results always change and inconsistent

I'm trying to train and test my dataset using MLPRegressor. I have two datasets (train dataset and test dataset), both of them have the exact same columns of features and label. Here's the example of my datasets :

training dataset :

Full,Gold Standard
1.176,3.571
4.231,3.467
3.75,4.333
3.519,3.5
1.154,2.833
3.2,3.643
2.692,3.4
3.611,2.733
4.0,2.393
2.727,1.933
3.529,3.423
2.647,2.733
1.538,2.786
2.0,2.967
2.647,2.533
1.786,2.552
5.0,5.0
3.158,4.6
1.875,2.733

testing dataset :

Full,Gold Standard
1.667,2.345
3.056,1.9
1.765,2.2
0.714,0.0
1.538,2.586
2.188,1.667
3.333,2.8
2.5,2.481
1.667,2.433
1.842,0.0
2.381,0.793
0.588,1.0
1.176,1.433
1.538,2.3
0.588,1.655
0.909,2.333
0.833,3.333
1.111,2.5
0.0,2.067

and here's my code :

import csv
import numpy as np 
import random
import os.path

from sklearn import preprocessing as pre
from sklearn.neural_network import MLPRegressor

with open('FullFeatures2017.csv') as f:
    reader = csv.reader(f)
    next(reader, None)
    training = list(reader)
f.closed
#print("Row number of data training : ", len(training))

print

#------------training-------------------
train_data = [list(map(float, training[i])) for i in range(len(training))]
data1 = np.array(train_data)
print("Row number of training data : ", len(train_data))
X_train = data1[:, :-1]
y_train = data1[:, -1:]

scaler = pre.StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

print

#------------open csv data testing-------------------
with open('FullFeatures2016.csv') as f:
    reader = csv.reader(f)
    next(reader, None)
    testing = list(reader)
f.closed
#print("Row number data testing : ", len(testing))

print

#------------testing-------------------
test_data = [list(map(float, testing[i])) for i in range(len(testing))]
data2 = np.array(test_data)
print("Row number of testing data : ", len(test_data))
X_test = data2[:, :-1]
y_test = data2[:, -1:]

X_test_scaled = scaler.fit_transform(X_test)

print

#------------Model Training-------------------
mlp = MLPRegressor(max_iter=500, learning_rate_init=0.1, random_state=1, solver='lbfgs', tol=0.001 )
y_train2 = np.ravel(y_train)
mlp.fit(X_train_scaled, y_train2)
#print(mlp.fit(X_train, y_train2))

print

#------------Model Testing or Prediction-------------------
prediction = mlp.predict(X_test_scaled)
print len(prediction)
print
print prediction
print

it works but the results always change everytime I run the program. I have given the random_state number like 1, 2, or 3 (not 0), but still always change.

anyone know how to predict the same and consistent results using MLPRegressor?

Thanks