Fix bugs of dssm

dssm/README.cn.md

@@ -13,7 +13,7 @@ DSSM \[[1](##参考文献)\]是微软研究院13年提出来的经典的语义
 
 DSSM 已经发展成了一个框架，可以很自然地建模两个记录之间的距离关系，
 例如对于文本相关性问题，可以用余弦相似度 (cosin similarity) 来刻画语义距离；
-而对于搜索引擎的结果排序，可以在DSSM上接上Rank损失训练处一个排序模型。
+而对于搜索引擎的结果排序，可以在DSSM上接上Rank损失训练出一个排序模型。
 
 ## 模型简介
 在原论文\[[1](#参考文献)\]中，DSSM模型用来衡量用户搜索词 Query 和文档集合 Documents 之间隐含的语义关系，模型结构如下
@@ -165,7 +165,13 @@ def create_rnn(self, emb, prefix=''):
     '''
     A GRU sentence vector learner.
     '''
-    gru = paddle.layer.gru_memory(input=emb,)
+    gru = paddle.networks.simple_gru(
+        input=emb,
+        size=self.dnn_dims[1],
+        mixed_param_attr=ParamAttr(name='%s_gru_mixed.w' % prefix),
+        mixed_bias_param_attr=ParamAttr(name="%s_gru_mixed.b" % prefix),
+        gru_param_attr=ParamAttr(name='%s_gru.w' % prefix),
+        gru_bias_attr=ParamAttr(name="%s_gru.b" % prefix))
     sent_vec = paddle.layer.last_seq(gru)
     return sent_vec
 ```
@@ -184,7 +190,11 @@ def create_fc(self, emb, prefix=''):
     '''
     _input_layer = paddle.layer.pooling(
         input=emb, pooling_type=paddle.pooling.Max())
-    fc = paddle.layer.fc(input=_input_layer, size=self.dnn_dims[1])
+    fc = paddle.layer.fc(
+        input=_input_layer,
+        size=self.dnn_dims[1],
+        param_attr=ParamAttr(name='%s_fc.w' % prefix),
+        bias_attr=ParamAttr(name="%s_fc.b" % prefix))
     return fc
 ```
 
@@ -206,7 +216,6 @@ def create_dnn(self, sent_vec, prefix):
             fc = paddle.layer.fc(
                 input=_input_layer,
                 size=dim,
-                name=name,
                 act=paddle.activation.Tanh(),
                 param_attr=ParamAttr(name='%s.w' % name),
                 bias_attr=ParamAttr(name='%s.b' % name),
@@ -244,9 +253,9 @@ def _build_classification_or_regression_model(self, is_classification):
         if is_classification else paddle.data_type.dense_input)
 
     prefixs = '_ _'.split(
-    ) if self.share_semantic_generator else 'left right'.split()
+    ) if self.share_semantic_generator else 'source target'.split()
     embed_prefixs = '_ _'.split(
-    ) if self.share_embed else 'left right'.split()
+    ) if self.share_embed else 'source target'.split()
 
     word_vecs = []
     for id, input in enumerate([source, target]):
@@ -258,16 +267,21 @@ def _build_classification_or_regression_model(self, is_classification):
         x = self.model_arch_creater(input, prefix=prefixs[id])
         semantics.append(x)
 
-    concated_vector = paddle.layer.concat(semantics)
-    prediction = paddle.layer.fc(
-        input=concated_vector,
-        size=self.class_num,
-        act=paddle.activation.Softmax())
-    cost = paddle.layer.classification_cost(
-        input=prediction,
-        label=label) if is_classification else paddle.layer.mse_cost(
-            prediction, label)
-    return cost, prediction, label
+    if is_classification:
+        concated_vector = paddle.layer.concat(semantics)
+        prediction = paddle.layer.fc(
+            input=concated_vector,
+            size=self.class_num,
+            act=paddle.activation.Softmax())
+        cost = paddle.layer.classification_cost(
+            input=prediction, label=label)
+    else:
+        prediction = paddle.layer.cos_sim(*semantics)
+        cost = paddle.layer.square_error_cost(prediction, label)
+
+    if not self.is_infer:
+        return cost, prediction, label
+    return prediction
 ```
 ### Pairwise Rank实现
 Pairwise Rank复用上面的DNN结构，同一个source对两个target求相似度打分，
@@ -297,7 +311,7 @@ def _build_rank_model(self):
         name='label_input', type=paddle.data_type.integer_value(1))
 
     prefixs = '_ _ _'.split(
-    ) if self.share_semantic_generator else 'source left right'.split()
+    ) if self.share_semantic_generator else 'source target target'.split()
     embed_prefixs = '_ _'.split(
     ) if self.share_embed else 'source target target'.split()
 

dssm/network_conf.py

@@ -96,14 +96,24 @@ class DSSM(object):
         '''
         _input_layer = paddle.layer.pooling(
             input=emb, pooling_type=paddle.pooling.Max())
-        fc = paddle.layer.fc(input=_input_layer, size=self.dnn_dims[1])
+        fc = paddle.layer.fc(
+            input=_input_layer,
+            size=self.dnn_dims[1],
+            param_attr=ParamAttr(name='%s_fc.w' % prefix),
+            bias_attr=ParamAttr(name="%s_fc.b" % prefix))
         return fc
 
     def create_rnn(self, emb, prefix=''):
         '''
         A GRU sentence vector learner.
         '''
-        gru = paddle.networks.simple_gru(input=emb, size=256)
+        gru = paddle.networks.simple_gru(
+            input=emb,
+            size=self.dnn_dims[1],
+            mixed_param_attr=ParamAttr(name='%s_gru_mixed.w' % prefix),
+            mixed_bias_param_attr=ParamAttr(name="%s_gru_mixed.b" % prefix),
+            gru_param_attr=ParamAttr(name='%s_gru.w' % prefix),
+            gru_bias_attr=ParamAttr(name="%s_gru.b" % prefix))
         sent_vec = paddle.layer.last_seq(gru)
         return sent_vec
 
@@ -147,7 +157,6 @@ class DSSM(object):
                 logger.info("create fc layer [%s] which dimention is %d" %
                             (name, dim))
                 fc = paddle.layer.fc(
-                    name=name,
                     input=_input_layer,
                     size=dim,
                     act=paddle.activation.Tanh(),
@@ -195,7 +204,7 @@ class DSSM(object):
                 name='label_input', type=paddle.data_type.integer_value(1))
 
         prefixs = '_ _ _'.split(
-        ) if self.share_semantic_generator else 'source left right'.split()
+        ) if self.share_semantic_generator else 'source target target'.split()
         embed_prefixs = '_ _'.split(
         ) if self.share_embed else 'source target target'.split()
 
@@ -249,9 +258,9 @@ class DSSM(object):
             if is_classification else paddle.data_type.dense_vector(1))
 
         prefixs = '_ _'.split(
-        ) if self.share_semantic_generator else 'left right'.split()
+        ) if self.share_semantic_generator else 'source target'.split()
         embed_prefixs = '_ _'.split(
-        ) if self.share_embed else 'left right'.split()
+        ) if self.share_embed else 'source target'.split()
 
         word_vecs = []
         for id, input in enumerate([source, target]):