在 tf2.0 中为什么使用 RandomizedSearchCV 报错？

我的代码如下:

def build_model(hidden_layers = 1, layer_size = 30, learning_rate = 3e-3): model = keras.models.Sequential() model.add(keras.layers.Dense(layer_size, activation='relu', input_shape=x_train.shape[1:])) for _ in range(hidden_layers - 1): model.add(keras.layers.Dense(layer_size, activation = 'relu')) model.add(keras.layers.Dense(1)) optimizer = keras.optimizers.SGD(learning_rate) model.compile(loss = 'mse', optimizer = optimizer) return model sklearn_model = tf.keras.wrappers.scikit_learn.KerasRegressor( build_fn = build_model) callbacks = [keras.callbacks.EarlyStopping(patience=5, min_delta=1e-2)] history = sklearn_model.fit(x_train_scaled, y_train, epochs = 10, validation_data = (x_valid_scaled, y_valid), callbacks = callbacks) from scipy.stats import reciprocal param_distribution = { "hidden_layers":[1, 2, 3, 4], "layer_size": np.arange(1, 100), "learning_rate": reciprocal(1e-4, 1e-2), } from sklearn.model_selection import RandomizedSearchCV random_search_cv = RandomizedSearchCV(sklearn_model, param_distribution, n_iter = 10, cv = 3, n_jobs = 1) random_search_cv.fit(x_train_scaled, y_train, epochs = 100, validation_data = (x_valid_scaled, y_valid), callbacks = callbacks) 

运行后，报错如下：“Cannot clone object , as the constructor either does not set or modifies parameter layer_size” 这是什么原因呢，还有设置 n_jobs > 1 时也会报错