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)
