使用MLP的Sklearn GridSearch将随机状态放在哪里？

Question

因此，我做了以下操作：

   MLP = MLPRegressor()
      parameter_space = {
'hidden_layer_sizes': [(32,), (32,16), (32,16,8), (32,16,8,4), (32,16,8,4,2), (32,32), (32,32,32), (32,32,32,32), (32,32,32,32,32), (16,8,4,2)],
'activation': ['relu'],
'solver': ['adam'],
'learning_rate_init': [1, 0.1, 0.01, 0.001,0.0001,0.00001],
'max_iter': [5000],
'shuffle': [True, False],
'random_state': [0],
'early_stopping': [True, False],
'n_iter_no_change': [50],
}

gs_MLP = GridSearchCV(estimator = MLP, param_grid= parameter_space, cv = 7, n_jobs = -1) 
gs_MLP_fit = gs_MLP.fit(X, y)
gs_MLP.score(X,y)

并且我注意到，每当我更改hidden_​​layer_size中的顺序时，它会提供不同的答案。首先，它说（16,8,4,2），当我将（16,8,4,2）结束时，它说（32,32,32,32）是最好的。

我认为这与Random_State有关吗？我必须将其放入mlpregressor（）中吗？如mlpregressor（random_state = 0）