def build_v1(hp: kt.HyperParameters, base_feature_size: int = 0):
spectral_size = hp.Choice("spectral_size",
values=[8, 16, 32, 64],
ordered=True)
dropout_rate = hp.Float("dropout_rate", 0.0, 0.8, step=0.1)
output_units = hp.Choice("embedding_size", [8, 16, 32, 64, 128],
ordered=True)
hidden_units = hp.Choice("hidden_units",
values=[32, 64, 128, 256, 512],
ordered=True)
hidden_layers = hp.Int("hidden_layers", min_value=1, max_value=3)
spec = tf.TensorSpec(
(
None,
spectral_size + base_feature_size,
),
dtype=tf.float32,
)
model = core.sgae(
spec,
functools.partial(
mlp,
output_units=output_units,
hidden_units=(hidden_units, ) * hidden_layers,
dropout_rate=dropout_rate,
),
)
_compile(hp, model)
return model
def Float(
hp: kt.HyperParameters,
name: str,
min_value: float,
max_value: float,
step: tp.Optional[float] = None,
sampling: tp.Optional[str] = None,
default: tp.Optional[float] = None,
parent_name: tp.Optional[str] = None,
parent_values=None,
):
return hp.Float(
name=name,
min_value=min_value,
max_value=max_value,
step=step,
sampling=sampling,
default=default,
parent_name=parent_name,
parent_values=parent_values,
)
"LSTM", {
"units":
DEFAULT_HP.Int(name='units',
min_value=32,
max_value=128,
step=32,
default=64),
"return_sequences":
False,
"kernel_initializer":
"glorot_uniform",
"activation":
DEFAULT_HP.Choice(name='LSTM_1_activation',
values=['relu', 'tanh', 'sigmoid', "linear"],
default='relu'),
}
],
[
"Dropout", {
"rate":
DEFAULT_HP.Float(name='dropout',
min_value=0.0,
max_value=0.5,
default=0.2,
step=0.05)
}
], ["Dense", {
"activation": "linear"
}]]
}