fix hyper_parameters

models/match/dssm/config.yaml

@@ -32,8 +32,8 @@ hyper_parameters:
     class: sgd
     learning_rate: 0.01
     strategy: async
-  TRIGRAM_D: 1000
-  NEG: 4
+  trigram_d: 1000
+  neg_num: 4
   fc_sizes: [300, 300, 128]
   fc_acts: ['tanh', 'tanh', 'tanh']
 

models/match/dssm/model.py

@@ -23,8 +23,8 @@ class Model(ModelBase):
         ModelBase.__init__(self, config)
 
     def _init_hyper_parameters(self):
-        self.TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D")
-        self.Neg = envs.get_global_env("hyper_parameters.NEG")
+        self.trigram_d = envs.get_global_env("hyper_parameters.trigram_d")
+        self.neg_num = envs.get_global_env("hyper_parameters.neg_num")
         self.hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes")
         self.hidden_acts = envs.get_global_env("hyper_parameters.fc_acts")
         self.learning_rate = envs.get_global_env(
@@ -33,12 +33,12 @@ class Model(ModelBase):
     def input_data(self, is_infer=False, **kwargs):
         query = fluid.data(
             name="query",
-            shape=[-1, self.TRIGRAM_D],
+            shape=[-1, self.trigram_d],
             dtype='float32',
             lod_level=0)
         doc_pos = fluid.data(
             name="doc_pos",
-            shape=[-1, self.TRIGRAM_D],
+            shape=[-1, self.trigram_d],
             dtype='float32',
             lod_level=0)
 
@@ -48,9 +48,9 @@ class Model(ModelBase):
         doc_negs = [
             fluid.data(
                 name="doc_neg_" + str(i),
-                shape=[-1, self.TRIGRAM_D],
+                shape=[-1, self.trigram_d],
                 dtype="float32",
-                lod_level=0) for i in range(self.Neg)
+                lod_level=0) for i in range(self.neg_num)
         ]
         return [query, doc_pos] + doc_negs