[QUESTION] Regarding Hyperparameter Tuning of NN with keras/sklearn

Hi,
first of all, thanks for this amazing book. I have a question regarding chapter 10, hyperparameter tuning with keras and sklearn:
the model allows for multiple hidden layers. However, I believe that `n_neurons` is fixed across all hidden layers. How can I make the model more flexible so that `n_neurons` can change with every layer?

Best,
Chris

```python
def build_model(n_hidden=1, n_neurons=30, learning_rate=3e-3, input_shape=[8]): # <=== n_neurons ???
model = keras.models.Sequential()
model.add(keras.layers.InputLayer(input_shape=input_shape))
for layer in range(n_hidden):
model.add(keras.layers.Dense(n_neurons, activation="relu")) # <=== ???
model.add(keras.layers.Dense(1))
optimizer = keras.optimizers.SGD(learning_rate=learning_rate)
model.compile(loss="mse", optimizer=optimizer)
return model

keras_reg = keras.wrappers.scikit_learn.KerasRegressor(build_model)

keras_reg.fit(X_train, y_train, epochs=100,
validation_data=(X_valid, y_valid),
callbacks=[keras.callbacks.EarlyStopping(patience=10)])

from scipy.stats import reciprocal
from sklearn.model_selection import RandomizedSearchCV

param_distribs = {
"n_hidden": [0, 1, 2, 3],
**"n_neurons": np.arange(1, 100)** .tolist(),
"learning_rate": reciprocal(3e-4, 3e-2) .rvs(1000).tolist(),
}

rnd_search_cv = RandomizedSearchCV(keras_reg, param_distribs, n_iter=10, cv=3, verbose=2)
rnd_search_cv.fit(X_train, y_train, epochs=100,
validation_data=(X_valid, y_valid),
callbacks=[keras.callbacks.EarlyStopping(patience=10)])
```