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