from  sklearn.neural_network import MLPClassifier
 print('开始训练....')
 print(time.strftime('%Y-%m-%d %H:%M:%S'))
 # 神经网络分类预测
 mlp = MLPClassifier(solver='sgd', activation='relu',alpha=1e-4,hidden_layer_sizes=(10,10), random_state=1,max_iter=500,verbose=10,learning_rate_init=.001)
 # 训练模型
 mlp.fit(x_train, y_train)
 print('结束训练....')
 print(time.strftime('%Y-%m-%d %H:%M:%S'))
 
 # 评估模型
 y_pred = mlp.predict(x_test)
 print("预测准确度:{:.2f}".format(mlp.score(x_test,y_test)))

 print(pd.crosstab(y_test, y_pred, rownames=['Actual Values'], colnames=['Prediction']))
 # 输出测试集的预测结果与真实值的评估报告
 print(classification_report(y_test,  y_pred))