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conf.py
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conf.py
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__author__ = 'liuyuemaicha'
class GSTConfig(object):
beam_size = 7
learning_rate = 0.5
learning_rate_decay_factor = 0.99
max_gradient_norm = 5.0
batch_size = 256
emb_dim = 1024
num_layers = 2
vocab_size = 25000
train_dir = "./gst_data/"
name_model = "st_model"
tensorboard_dir = "./tensorboard/gst_log/"
name_loss = "gst_loss"
max_train_data_size = 0
steps_per_checkpoint = 200
buckets = [(5, 10), (10, 15), (20, 25), (40, 50)]
buckets_concat = [(5, 10), (10, 15), (20, 25), (40, 50), (100, 50)]
class GCCConfig(object):
beam_size = 7
learning_rate = 0.5
learning_rate_decay_factor = 0.99
max_gradient_norm = 5.0
batch_size = 128
emb_dim = 1024
num_layers = 2
vocab_size = 25000
train_dir = "./gcc_data/"
name_model = "cc_model"
tensorboard_dir = "./tensorboard/gcc_log/"
name_loss = "gcc_loss"
max_train_data_size = 0
steps_per_checkpoint = 200
buckets = [(10, 10), (20, 15), (40, 25), (80, 50)]
buckets_concat = [(10, 10), (20, 15), (40, 25), (80, 50), (100,50)]
class GBKConfig(object):
beam_size = 7
learning_rate = 0.5
learning_rate_decay_factor = 0.99
max_gradient_norm = 5.0
batch_size = 256
emb_dim = 1024
num_layers = 2
vocab_size = 25000
train_dir = "./gbk_data/"
name_model = "bk_model"
tensorboard_dir = "./tensorboard/gbk_log/"
name_loss = "gbk_loss"
max_train_data_size = 0
steps_per_checkpoint = 200
buckets = [(10, 5), (15, 10), (25, 20), (50, 40)]
buckets_concat = [(10, 5), (15, 10), (25, 20), (50, 40), (100, 50)]
class GRLConfig(object):
beam_size = 7
learning_rate = 0.5
learning_rate_decay_factor = 0.99
max_gradient_norm = 5.0
batch_size = 256
emb_dim = 1024
num_layers = 2
vocab_size = 25000
train_dir = "./grl_data/"
name_model = "rl_model"
tensorboard_dir = "./tensorboard/grl_log/"
name_loss = "grl_loss"
pre_name_loss = "pre_rl_loss"
max_train_data_size = 0
steps_per_checkpoint = 200
buckets = [(5, 10), (10, 15), (20, 25), (40, 50)]
buckets_concat = [(5, 10), (10, 15), (20, 25), (40, 50), (100, 50)]
class Pre_GRLConfig(object):
beam_size = 4
learning_rate = 0.5
learning_rate_decay_factor = 0.99
max_gradient_norm = 5.0
batch_size = 10
emb_dim = 512
num_layers = 2
vocab_size = 1000
train_dir = "./pre_grl_data/"
name_model = "rl_model"
tensorboard_dir = "./tensorboard/grl_log/"
name_loss = "grl_loss"
pre_name_loss = "pre_rl_loss"
max_train_data_size = 0
steps_per_checkpoint = 200
buckets = [(5, 10), (10, 15), (20, 25), (40, 50)]
buckets_concat = [(5, 10), (10, 15), (20, 25), (40, 50), (100, 50)]