def __init__(self, hidden_units=None, reduce_factor=None, dnn_dropout=None, use_bn=None, dnn_layers=None, activation=None, space=None, name=None, **hyperparams): if hidden_units is None: hidden_units = Choice([100, 200, 300, 500, 800, 1000]) hyperparams['hidden_units'] = hidden_units if reduce_factor is None: reduce_factor = Choice([1, 0.8, 0.5]) hyperparams['reduce_factor'] = reduce_factor if dnn_dropout is None: dnn_dropout = Choice([0, 0.1, 0.3, 0.5]) hyperparams['dnn_dropout'] = dnn_dropout if use_bn is None: use_bn = Bool() hyperparams['use_bn'] = use_bn if dnn_layers is None: dnn_layers = Choice([1, 2, 3]) hyperparams['dnn_layers'] = dnn_layers if activation is None: activation = 'relu' hyperparams['activation'] = activation ModuleSpace.__init__(self, space, name, **hyperparams)
def __init__(self, batch_size=128, epochs=None, space=None, name=None, **hyperparams): if batch_size is None: batch_size = Choice([128, 256, 512]) hyperparams['batch_size'] = batch_size if epochs is not None: hyperparams['epochs'] = epochs ModuleSpace.__init__(self, space, name, **hyperparams) self.space.fit_params = self
def __init__(self, filters, name_prefix, data_format=None, space=None, name=None, **hyperparams): self.filters = filters self.name_prefix = name_prefix self.data_format = data_format ModuleSpace.__init__(self, space, name, **hyperparams)
def __init__(self, space=None, name=None, **hyperparams): ModuleSpace.__init__(self, space, name, **hyperparams) self.space.DT_Module = self self.config = None
def __init__(self, transformer=None, space=None, name=None, **hyperparams): self.transformer = transformer ModuleSpace.__init__(self, space, name, **hyperparams)
def __init__(self, task, fit_kwargs, space=None, name=None, **hyperparams): ModuleSpace.__init__(self, space, name, **hyperparams) self.task = task self.fit_kwargs = fit_kwargs self.estimator = None