# RandomizedSearchCV
# 1. 转化为sklearn的model
# 2. 定义参数集合
# 3. 搜索参数

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 = 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)

老师，这段代码中： for _ in range(hidden_layers - 1):
hidden_layers - 1为什么要减1？隐藏层不是除了输入层和输出层之外的层次吗？它的前面的那一层中指明了input_shape它就是输入层呀，难道还有别的输入层？我没看到。