Keras Tuner on autoencoder - Add condition : first hidden layer units greater than or equal next hidden layer units - deep-learning

Question

I want to use Keras-tuner to tune an autoencoder hyperparameters.
It is a symetric AE with two layers. I want the number of units in the first layer always greater than or equal the units in the second layer. But I don't know how implement it with keras-tuner. If someone can help, it would be very great. Thank you in advance.
class DAE(tf.keras.Model):
'''
A DAE model
'''
def __init__(self, hp, **kwargs):
'''
DAE instantiation
args :
hp : Tuner
input_dim : input dimension
return:
None
'''
super(DAE, self).__init__(**kwargs)
input_dim = 15
latent_dim = hp.Choice("latent_space", [2,4,8])
units_0 = hp.Choice("units_0", [8, 16, 32, 64])
units_1 = hp.Choice("units_1", [8, 16, 32, 64])
for i in [8, 16, 32, 64]:
with hp.conditional_scope("units_0", [i]):
if units_0 == i:
......? # units_1 should be <= i
dropout = hp.Choice("dropout_rate", [0.1, 0.2, 0.3, 0.4, 0.5])
inputs = tf.keras.Input(shape = (input_dim,))
x = layers.Dense(units_0, activation="relu")(inputs)
x = layers.Dropout(dropout)(x)
x = layers.Dense(units_1, activation="relu")(x)
x = layers.Dropout(dropout)(x)
z = layers.Dense(latent_dim)(x)
self.encoder = tf.keras.Model(inputs, z, name="encoder")
inputs = tf.keras.Input(shape=(latent_dim,))
x = layers.Dense(units_1, activation="relu")(inputs)
x = layers.Dropout(dropout)(x)
x = layers.Dense(units_0, activation="relu")(x)
x = layers.Dropout(dropout)(x)
outputs = layers.Dense(input_dim, activation="linear")(x)
self.decoder = tf.keras.Model(inputs, outputs, name="decoder")```
See above my code. It's a denoising autoencoder class

Answer 1

I found the solution. We need to create differents units_1 for for each units_O values
class DAE(tf.keras.Model):
'''
A DAE model
'''
def __init__(self, hp, training=None, **kwargs):
'''
DAE instantiation
args :
hp : Tuner
input_dim : input dimension
return:
None
'''
super(DAE, self).__init__(**kwargs)
self.input_dim = 15
l_units = [16, 32, 64, 128]
latent_dim = hp.Choice("latent_space", [2,4,8])
units_0 = hp.Choice("units_0", l_units)
dropout_0 = hp.Choice("dropout_rate_0", [0.1, 0.2, 0.3, 0.4, 0.5])
dropout_1 = hp.Choice("dropout_rate_1", [0.1, 0.2, 0.3, 0.4, 0.5])
for i in l_units:
name = "units_1_%d" % i # generates unique name for each hp.Int object
with hp.conditional_scope("units_0", [i]):
if units_0 == i:
locals()[name] = hp.Int(name, min_value = 8, max_value = i, step = 2, sampling = "log" )
inputs = tf.keras.Input(shape = (self.input_dim,))
x = layers.Dense(units_0, activation="relu")(inputs)
x = layers.Dropout(dropout_0)(x, training=training)
x = layers.Dense(locals()[name], activation="relu")(x)
x = layers.Dropout(dropout_1)(x, training=training)
z = layers.Dense(latent_dim)(x)
self.encoder = tf.keras.Model(inputs, z, name="encoder")
inputs = tf.keras.Input(shape=(latent_dim,))
x = layers.Dense(locals()[name], activation="relu")(inputs)
x = layers.Dropout(dropout_1)(x, training=training)
x = layers.Dense(units_0, activation="relu")(x)
x = layers.Dropout(dropout_0)(x, training=training)
outputs = layers.Dense(self.input_dim, activation="linear")(x)
self.decoder = tf.keras.Model(inputs, outputs, name="decoder")