我在为我的MLPClassifier模型进行超参数调优。我尝试使用网格搜索，但出了些问题，我无法找出原因。
这是我的模型：
data = pd.read_csv("Xy_train.csv")data = data.loc[(data.age <= 90) & (data.thal != 0)]x = data.drop(columns=['y', 'id'])y = data.yx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=986)scaler = StandardScaler()x_train_s = scaler.fit_transform(x_train)x_test_s = scaler.fit_transform(x_test)model = MLPClassifier(random_state=986, hidden_layer_sizes=(15), max_iter=200, activation='relu', verbose=True, learning_rate_init=0.01)model.fit(x_train_s, y_train)x_train = np.linspace(-5, 5, 50)y_train = np.linspace(-5, 5, 50)predictions = pd.DataFrame()print(f"Accuracy: {accuracy_score(y_true=y_test, y_pred=model.predict(x_test_s)):.3f}")print(confusion_matrix(y_true=y_test, y_pred=model.predict(x_test_s)))这是我的网格搜索代码：
epochs = [5, 10]batches = [5, 10, 100]optimizers = ['lbfgs', 'sgd', 'adam']hyperparameters = dict(optimizer=optimizers, epochs=epochs, batch_size=batches)grid = GridSearchCV(estimator=model, cv=3, param_grid=hyperparameters)grid.fit(x_train, y_train)我收到了一堆错误信息，这些信息没有提供太多有用的内容。我认为最相关的错误可能是：使用estimator.get_params().keys()检查可用的参数列表。错误信息如下：
line 49, in <module> grid.fit(x_train, y_train)line 754, in _dispatch job = self._backend.apply_async(batch, callback=cb)line 209, in apply_async result = ImmediateResult(func) line 233, in set_params raise ValueError('Invalid parameter %s for estimator %s. 'ValueError: Invalid parameter epochs for estimator MLPClassifier(activation='relu', alpha=0.0001, batch_size=5, beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08, hidden_layer_sizes=15, learning_rate='constant', learning_rate_init=0.01, max_fun=15000, max_iter=200, momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5, random_state=986, shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1, verbose=True, warm_start=False). Check the list of available parameters with `estimator.get_params().keys()'但我确实检查了参数键，看起来是正常的。
回答：
在你的hyperparameters网格中：
hyperparameters = dict(optimizer=optimizers, epochs=epochs, batch_size=batches)你请求的超参数如optimizer和epochs是不存在的，因此引发了错误。
根据文档，你应该分别使用solver和max_iter，即：
hyperparameters = dict(solver=optimizers, max_iter=epochs, batch_size=batches)