Update default hyper-params of scorer in python scripts

deploy/demo_server.py

@@ -23,8 +23,8 @@ add_arg('beam_size',        int,    500,    "Beam search width.")
 add_arg('num_conv_layers',  int,    2,      "# of convolution layers.")
 add_arg('num_rnn_layers',   int,    3,      "# of recurrent layers.")
 add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
-add_arg('alpha',            float,  2.15,   "Coef of LM for beam search.")
-add_arg('beta',             float,  0.35,   "Coef of WC for beam search.")
+add_arg('alpha',            float,  2.5,   "Coef of LM for beam search.")
+add_arg('beta',             float,  0.3,   "Coef of WC for beam search.")
 add_arg('cutoff_prob',      float,  1.0,    "Cutoff probability for pruning.")
 add_arg('cutoff_top_n',     int,    40,     "Cutoff number for pruning.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of simple RNNs.")

infer.py

@@ -21,8 +21,8 @@ add_arg('num_proc_bsearch', int,    8,      "# of CPUs for beam search.")
 add_arg('num_conv_layers',  int,    2,      "# of convolution layers.")
 add_arg('num_rnn_layers',   int,    3,      "# of recurrent layers.")
 add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
-add_arg('alpha',            float,  2.15,   "Coef of LM for beam search.")
-add_arg('beta',             float,  0.35,   "Coef of WC for beam search.")
+add_arg('alpha',            float,  2.5,    "Coef of LM for beam search.")
+add_arg('beta',             float,  0.3,    "Coef of WC for beam search.")
 add_arg('cutoff_prob',      float,  1.0,    "Cutoff probability for pruning.")
 add_arg('cutoff_top_n',     int,    40,     "Cutoff number for pruning.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of simple RNNs.")

test.py

@@ -22,8 +22,8 @@ add_arg('num_proc_data',    int,    8,      "# of CPUs for data preprocessing.")
 add_arg('num_conv_layers',  int,    2,      "# of convolution layers.")
 add_arg('num_rnn_layers',   int,    3,      "# of recurrent layers.")
 add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
-add_arg('alpha',            float,  2.15,   "Coef of LM for beam search.")
-add_arg('beta',             float,  0.35,   "Coef of WC for beam search.")
+add_arg('alpha',            float,  2.5,    "Coef of LM for beam search.")
+add_arg('beta',             float,  0.3,    "Coef of WC for beam search.")
 add_arg('cutoff_prob',      float,  1.0,    "Cutoff probability for pruning.")
 add_arg('cutoff_top_n',     int,    40,     "Cutoff number for pruning.")
 add_arg('use_gru',          bool,   False,  "Use GRUs instead of simple RNNs.")