def get_cfg(key=None): if key is None: cfg = CfgNode() cfg.feature_units = -1 # -1 means not given and we will use the units of BERT # TODO(sxjscience) Use a class to store the TextNet cfg.text_net = CfgNode() cfg.text_net.use_segment_id = True cfg.text_net.pool_type = 'cls' cfg.agg_net = FeatureAggregator.get_cfg() cfg.categorical_net = CategoricalFeatureNet.get_cfg() cfg.numerical_net = NumericalFeatureNet.get_cfg() cfg.initializer = CfgNode() cfg.initializer.weight = ['truncnorm', 0, 0.02] cfg.initializer.bias = ['zeros'] return cfg else: raise NotImplementedError
def get_cfg(key=None): if key is None: cfg = CfgNode() cfg.base_feature_units = -1 # -1 means not given and we will use the units of BERT cfg.text_net = CfgNode() cfg.text_net.use_segment_id = True cfg.text_net.pool_type = 'cls' cfg.aggregate_categorical = True # Whether to use one network to aggregate the categorical columns. cfg.categorical_agg = CfgNode() cfg.categorical_agg.activation = 'leaky' cfg.categorical_agg.mid_units = 128 cfg.categorical_agg.num_layers = 1 cfg.categorical_agg.dropout = 0.1 cfg.categorical_agg.gated_activation = False cfg.agg_net = FeatureAggregator.get_cfg() cfg.categorical_net = CategoricalFeatureNet.get_cfg() cfg.numerical_net = NumericalFeatureNet.get_cfg() cfg.initializer = CfgNode() cfg.initializer.weight = ['xavier', 'uniform', 'avg', 3.0] cfg.initializer.bias = ['zeros'] return cfg else: raise NotImplementedError