train code of dssm model

recommend-model-produce/src/main/python/models/dssm/net.py

@@ -99,19 +99,16 @@ class DSSMLayer(nn.Layer):
         return paddle.concat(embedded_features, axis=1)
 
     def forward(self, left_features, right_features):
-        # 获取两个视频的特征表示
-        #paddle.static.Print(left_features, message="lqc left model input shape:")
-        #paddle.static.Print(right_features, message="lqc right model input shape:")        
+        # 获取两个视频的特征表示      
         left_vec, right_vec = self.get_vectors(left_features, right_features)
-        #paddle.static.Print(left_vec, message="lqc left model output shape:")
-        #paddle.static.Print(right_vec, message="lqc right model output shape:")
+
         # 计算相似度
         sim_score = F.cosine_similarity(
             left_vec, 
             right_vec, 
             axis=1
         ).reshape([-1, 1])
-        #paddle.static.Print(sim_score, message="lqc sim_score shape:")
+
         return sim_score, left_vec, right_vec
 
     def get_vectors(self, left_features, right_features):
@@ -124,22 +121,22 @@ class DSSMLayer(nn.Layer):
         left_vec = paddle.reshape(left_embedded, [-1, self.feature_num * self.embedding_dim])
               
         
-        #paddle.static.Print(left_vec, message=f"lqc lqc left_vec:")
+
         
         
         
         for i, layer in enumerate(self._left_tower):
             left_vec = layer(left_vec)
-            #paddle.static.Print(left_vec, message=f"After left layer {i}:")
+
         
         # 处理右视频特征
         right_embedded = self._process_features(right_features, self.right_embeddings)
         # right_vec = right_embedded
         right_vec = paddle.reshape(right_embedded, [-1, self.feature_num * self.embedding_dim])  
-        #paddle.static.Print(right_vec, message=f"lqc lqc left_vec:")
+
         for layer in self._right_tower:
             right_vec = layer(right_vec)
-            #paddle.static.Print(right_vec, message=f"After left layer {i}:")
+
             
         # 确保输出是L2归一化的
         left_vec = F.normalize(left_vec, p=2, axis=1)

recommend-model-produce/src/main/python/models/dssm/static_model.py

@@ -57,9 +57,7 @@ class StaticModel():
             left_features, right_features = input
         else:
             label,left_features, right_features = input
-            #paddle.static.Print(left_features, message="lqc left data feature shape:")
-            #paddle.static.Print(right_features, message="lqc right data feature shape:")
-            #paddle.static.Print(label, message="lqc label feature shape:")
+
 
         # 获取相似度和特征向量
         sim_score, left_vec, right_vec = dssm_model(left_features, right_features)