def _init_hyper_parameters(self): self.is_distributed = False self.distributed_embedding = False if envs.get_fleet_mode().upper() == "PSLIB": self.is_distributed = True if envs.get_global_env("hyper_parameters.distributed_embedding", 0) == 1: self.distributed_embedding = True self.sparse_feature_number = envs.get_global_env( "hyper_parameters.sparse_feature_number") self.sparse_feature_dim = envs.get_global_env( "hyper_parameters.sparse_feature_dim") self.sparse_inputs_slot = envs.get_global_env( "hyper_parameters.sparse_inputs_slots") self.dense_input_dim = envs.get_global_env( "hyper_parameters.dense_input_dim") self.learning_rate = envs.get_global_env( "hyper_parameters.optimizer.learning_rate") self.fc_sizes = envs.get_global_env("hyper_parameters.fc_sizes") self.use_embedding_gate = envs.get_global_env( 'hyper_parameters.use_embedding_gate') self.use_hidden_gate = envs.get_global_env( 'hyper_parameters.use_hidden_gate')
def _init_hyper_parameters(self): self.is_distributed = True if envs.get_fleet_mode().upper( ) == "PSLIB" else False self.sparse_feature_number = envs.get_global_env( "hyper_parameters.sparse_feature_number") self.sparse_feature_dim = envs.get_global_env( "hyper_parameters.sparse_feature_dim") self.learning_rate = envs.get_global_env( "hyper_parameters.optimizer.learning_rate")
def _init_hyper_parameters(self): self.is_distributed = True if envs.get_fleet_mode().upper( ) == "PSLIB" else False self.sparse_feature_number = envs.get_global_env( "hyper_parameters.sparse_feature_number", None) self.sparse_feature_dim = envs.get_global_env( "hyper_parameters.sparse_feature_dim", None) self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4) self.num_field = envs.get_global_env("hyper_parameters.num_field", None)
def _init_hyper_parameters(self): self.is_distributed = True if envs.get_fleet_mode().upper( ) == "PSLIB" else False self.sparse_feature_number = envs.get_global_env( "hyper_parameters.sparse_feature_number", None) self.sparse_feature_dim = envs.get_global_env( "hyper_parameters.sparse_feature_dim", None) self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", False) self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4) self.num_field = envs.get_global_env("hyper_parameters.num_field", None) self.hidden1_attention_size = envs.get_global_env( "hyper_parameters.hidden1_attention_size", 16) self.attention_act = envs.get_global_env("hyper_parameters.act", "relu")
def _init_hyper_parameters(self): self.is_distributed = True if envs.get_fleet_mode().upper( ) == "PSLIB" else False self.sparse_feature_number = envs.get_global_env( "hyper_parameters.sparse_feature_number") self.sparse_feature_dim = envs.get_global_env( "hyper_parameters.sparse_feature_dim") self.neg_num = envs.get_global_env("hyper_parameters.neg_num") self.with_shuffle_batch = envs.get_global_env( "hyper_parameters.with_shuffle_batch") self.learning_rate = envs.get_global_env( "hyper_parameters.optimizer.learning_rate") self.decay_steps = envs.get_global_env( "hyper_parameters.optimizer.decay_steps") self.decay_rate = envs.get_global_env( "hyper_parameters.optimizer.decay_rate")
def _init_hyper_parameters(self): self.is_distributed = True if envs.get_fleet_mode().upper( ) == "PSLIB" else False self.sparse_feature_number = envs.get_global_env( "hyper_parameters.sparse_feature_number", None) self.sparse_feature_dim = envs.get_global_env( "hyper_parameters.sparse_feature_dim", None) self.deep_input_size = envs.get_global_env( "hyper_parameters.deep_input_size", 50) self.use_inner_product = envs.get_global_env( "hyper_parameters.use_inner_product", None) self.layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes", None) self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4) self.num_field = envs.get_global_env("hyper_parameters.num_field", None) self.act = envs.get_global_env("hyper_parameters.act", None)
def _init_hyper_parameters(self): self.is_distributed = True if envs.get_fleet_mode().upper( ) == "PSLIB" else False self.sparse_feature_number = envs.get_global_env( "hyper_parameters.sparse_feature_number", None) self.sparse_feature_dim = envs.get_global_env( "hyper_parameters.sparse_feature_dim", None) self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", False) self.use_batchnorm = envs.get_global_env( "hyper_parameters.use_batchnorm", False) self.filters = envs.get_global_env("hyper_parameters.filters", [38, 40, 42, 44]) self.filter_size = envs.get_global_env("hyper_parameters.filter_size", [1, 9]) self.pooling_size = envs.get_global_env( "hyper_parameters.pooling_size", [2, 2, 2, 2]) self.new_filters = envs.get_global_env("hyper_parameters.new_filters", [3, 3, 3, 3]) self.hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes") self.num_field = envs.get_global_env("hyper_parameters.num_field", None) self.act = envs.get_global_env("hyper_parameters.act", None)
def _init_hyper_parameters(self): self.is_distributed = True if envs.get_fleet_mode().upper( ) == "PSLIB" else False self.sparse_feature_number = envs.get_global_env( "hyper_parameters.sparse_feature_number", None) self.sparse_feature_dim = envs.get_global_env( "hyper_parameters.sparse_feature_dim", None) self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", False) self.use_batchnorm = envs.get_global_env( "hyper_parameters.use_batchnorm", False) self.use_dropout = envs.get_global_env("hyper_parameters.use_dropout", False) self.dropout_prob = envs.get_global_env( "hyper_parameters.dropout_prob", None) self.layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes", None) self.loss_type = envs.get_global_env("hyper_parameters.loss_type", 'logloss') self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4) self.num_field = envs.get_global_env("hyper_parameters.num_field", None) self.act = envs.get_global_env("hyper_parameters.act", None)