Conflicting values for Best model; or how to interpet the output

[deprekate]

Describe the bug
Getting None for some Best Values

Below is the output for the best model, it says that 4 conv_layers is the best, but then has 'None' values for the size and kernal of the layer ("filters_3" and "kernel_3")

Search: Running Trial #16

Value             |Best Value So Far |Hyperparameter
4                 |4                 |conv_layers
88                |80                |filters_0
128               |96                |filters_1
96                |72                |filters_2
120               |None              |filters_3
5                 |8                 |kernels_0
6                 |10                |kernels_1
6                 |10                |kernels_2
11                |None              |kernels_3
1                 |3                 |dense_layers
0.09              |0.09              |dropout
128               |120               |neurons_0
72                |104               |neurons_1
112               |88                |neurons_2
1                 |1                 |tuner/epochs
0                 |0                 |tuner/bracket
0                 |0                 |tuner/initial_epoch
0                 |0                 |tuner/round

To Reproduce
This is the code for the HyperModel

class HyperRegressor(kt.HyperModel):
    def build(self, hp):
        inputs = tf.keras.layers.Input(shape=(99,), dtype=tf.int32)
        x = tf.keras.layers.Lambda(lambda x: tf.one_hot(x,depth=6), name='one_hot')(inputs)
        for i in range(hp.Int("conv_layers", 2, 6, default=3)):
            x = tf.keras.layers.Conv1D(
                filters     = hp.Int(f"filters_{i}", 72, 128, step=8, default=96),
                kernel_size = hp.Int(f"kernels_{i}",  3,  12, step=1, default= 7),
                activation  = "relu",
                padding     = "same",
            )(x)
        x = tf.keras.layers.Flatten()(x)
        d = hp.Float("dropout", 0.00, 0.10, step=0.01, default=0.05)
        for i in range(hp.Int("dense_layers", 1, 3, default=3)):
            x = tf.keras.layers.Dense(
                units=hp.Int(f"neurons_{i}", min_value=72, max_value=128, step=8),
                activation='relu'
            )(x)
            x = tf.keras.layers.Dropout(
                rate=d
            )(x)
        outputs = tf.keras.layers.Dense(3, activation='softmax')(x)
        model = tf.keras.models.Model(inputs, outputs)
        model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
        return model

Expected behavior
That None should only show up as a Best Value when that value is not used.

Additional context

Would you like to help us fix it?
Yes

[deprekate]

I figured I can avoid the error with None values by making the default value the highest value:

for i in range(hp.Int("conv_layers", 2, 6, default=6):