def __init__(
self,
max_features: int = 20001,
pretraining: Optional[Union[str, hyperparameters.Choice]] = None,
embedding_dim: Optional[Union[int, hyperparameters.Choice]] = None,
dropout: Optional[Union[float, hyperparameters.Choice]] = None,
**kwargs,
):
super().__init__(**kwargs)
self.max_features = max_features
self.pretraining = utils.get_hyperparameter(
pretraining,
hyperparameters.Choice(
"pretraining",
["random", "glove", "fasttext", "word2vec", "none"],
default="none",
),
str,
)
self.embedding_dim = utils.get_hyperparameter(
embedding_dim,
hyperparameters.Choice(
"embedding_dim", [32, 64, 128, 256, 512], default=128
),
int,
)
self.dropout = utils.get_hyperparameter(
dropout,
hyperparameters.Choice("dropout", [0.0, 0.25, 0.5], default=0.25),
float,
)
def __init__(
self,
num_layers: Optional[Union[int, hyperparameters.Choice]] = None,
num_units: Optional[Union[int, hyperparameters.Choice]] = None,
use_batchnorm: Optional[bool] = None,
dropout: Optional[Union[float, hyperparameters.Choice]] = None,
**kwargs,
):
super().__init__(**kwargs)
self.num_layers = utils.get_hyperparameter(
num_layers,
hyperparameters.Choice("num_layers", [1, 2, 3], default=2),
int,
)
self.num_units = utils.get_hyperparameter(
num_units,
hyperparameters.Choice(
"num_units", [16, 32, 64, 128, 256, 512, 1024], default=32
),
int,
)
self.use_batchnorm = use_batchnorm
self.dropout = utils.get_hyperparameter(
dropout,
hyperparameters.Choice("dropout", [0.0, 0.25, 0.5], default=0.0),
float,
)
def __init__(
self,
return_sequences: bool = False,
bidirectional: Optional[Union[bool, hyperparameters.Boolean]] = None,
num_layers: Optional[Union[int, hyperparameters.Choice]] = None,
layer_type: Optional[Union[str, hyperparameters.Choice]] = None,
**kwargs,
):
super().__init__(**kwargs)
self.return_sequences = return_sequences
self.bidirectional = utils.get_hyperparameter(
bidirectional,
hyperparameters.Boolean("bidirectional", default=True),
bool,
)
self.num_layers = utils.get_hyperparameter(
num_layers,
hyperparameters.Choice("num_layers", [1, 2, 3], default=2),
int,
)
self.layer_type = utils.get_hyperparameter(
layer_type,
hyperparameters.Choice("layer_type", ["gru", "lstm"], default="lstm"),
str,
)
def __init__(
self,
kernel_size: Optional[Union[int, hyperparameters.Choice]] = None,
num_blocks: Optional[Union[int, hyperparameters.Choice]] = None,
num_layers: Optional[int] = None,
filters: Optional[Union[int, hyperparameters.Choice]] = None,
max_pooling: Optional[bool] = None,
separable: Optional[bool] = None,
dropout: Optional[float] = None,
**kwargs,
):
super().__init__(**kwargs)
self.kernel_size = utils.get_hyperparameter(
kernel_size,
hyperparameters.Choice("kernel_size", [3, 5, 7], default=3),
int,
)
self.num_blocks = utils.get_hyperparameter(
num_blocks,
hyperparameters.Choice("num_blocks", [1, 2, 3], default=2),
int,
)
self.num_layers = num_layers
self.filters = utils.get_hyperparameter(
filters,
hyperparameters.Choice("filters", [16, 32, 64, 128, 256, 512],
default=32),
int,
)
self.max_pooling = max_pooling
self.separable = separable
self.dropout = dropout
def test_get_hyperparameter_with_hp_return_same():
hp = utils.get_hyperparameter(
hyperparameters.Choice("hp", [10, 30]),
hyperparameters.Choice("hp", [10, 20]),
int,
)
assert isinstance(hp, hyperparameters.Choice)
def __init__(
self,
max_sequence_length: Optional[Union[int, hyperparameters.Choice]] = None,
**kwargs,
):
super().__init__(**kwargs)
self.max_sequence_length = utils.get_hyperparameter(
max_sequence_length,
hyperparameters.Choice(
"max_sequence_length", [128, 256, 512], default=128
),
int,
)
def __init__(
self,
translation_factor: Optional[
Union[float, Tuple[float, float], hyperparameters.Choice]
] = None,
vertical_flip: Optional[bool] = None,
horizontal_flip: Optional[bool] = None,
rotation_factor: Optional[Union[float, hyperparameters.Choice]] = None,
zoom_factor: Optional[
Union[float, Tuple[float, float], hyperparameters.Choice]
] = None,
contrast_factor: Optional[
Union[float, Tuple[float, float], hyperparameters.Choice]
] = None,
**kwargs
):
super().__init__(**kwargs)
self.translation_factor = utils.get_hyperparameter(
translation_factor,
hyperparameters.Choice("translation_factor", [0.0, 0.1]),
Union[float, Tuple[float, float]],
)
self.horizontal_flip = horizontal_flip
self.vertical_flip = vertical_flip
self.rotation_factor = utils.get_hyperparameter(
rotation_factor,
hyperparameters.Choice("rotation_factor", [0.0, 0.1]),
float,
)
self.zoom_factor = utils.get_hyperparameter(
zoom_factor,
hyperparameters.Choice("zoom_factor", [0.0, 0.1]),
Union[float, Tuple[float, float]],
)
self.contrast_factor = utils.get_hyperparameter(
contrast_factor,
hyperparameters.Choice("contrast_factor", [0.0, 0.1]),
Union[float, Tuple[float, float]],
)
def test_get_hyperparameter_with_int_return_fixed():
hp = utils.get_hyperparameter(10, hyperparameters.Choice("hp", [10, 20]),
int)
assert isinstance(hp, hyperparameters.Fixed)
def test_get_hyperparameter_with_none_return_hp():
hp = utils.get_hyperparameter(None, hyperparameters.Choice("hp", [10, 20]),
int)
assert isinstance(hp, hyperparameters.Choice)
def test_get_hyperparameter_with_int_return_int():
value = utils.get_hyperparameter(10,
hyperparameters.Choice("hp",
[10, 20]), int)
assert isinstance(value, int)
assert value == 10
