[QUESTION] Use of Normalization layer with keras_tuner

[hahampis]

Hi,

in Chapter 10, there is this piece of code about (conditionally) using a normalization layer within the .fit() method of a kt.HyperModel:

class MyClassificationHyperModel(kt.HyperModel):
    def build(self, hp):
        return build_model(hp)

    def fit(self, hp, model, X, y, **kwargs):
        if hp.Boolean("normalize"):
            norm_layer = tf.keras.layers.Normalization()
            X = norm_layer(X)
        return model.fit(X, y, **kwargs)

My question is, shouldn't we call the .adapt() method of the Normalization layer before calling .fit()?
Also, I have found that using hp.Boolean() directly inside the .fit() method results in the normalize hyperparameter always being equal to False. If I initialize the parameter in the build() method instead and then use it inside fit(), then it can be either True or False during the search:

class MyClassificationHyperModel(kt.HyperModel):

    def build(self, hp: kt.HyperParameters) -> tf.keras.Model:
        normalize = hp.Boolean("normalize")
        ...

def fit(self, hp: kt.HyperParameters, model, X, y, **kwargs):
        normalize = hp.get("normalize")
        if normalize:
            norm_layer = tf.keras.layers.Normalization()
            norm_layer.adapt(X)
            X = norm_layer(X)
        return model.fit(X, y, **kwargs)

Versions:
OS: MacOSX 14.4.1
Python: 3.10.7
TensorFlow: 2.14
Keras-tuner: 1.4.7
Scikit-Learn: 1.14.1.post1